Transportation vehicle routing

ABSTRACT

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: determining a current transportation mode of a user with use of data of a user passenger vehicle associated to the user and data of a user auxiliary passenger vehicle associated to the user, wherein the user passenger vehicle is capable of carrying the user auxiliary vehicle, and wherein the user auxiliary vehicle is configured to be hand carried by the user; evaluating a current route of the user in dependence on the current transportation mode of the user as determined by the determining; and providing one or more output in dependence on the evaluating.

BACKGROUND

Location based services (LBS) are software services that use locationdata to control functionality of computer systems LBS informationservices have a number of uses, e.g. in social networking,entertainment, security, and in a plurality of additional applications.LBS services employ location services for locating mobile computersystems. Location services can incorporate a variety of differentlocating service technologies such as the Global Positioning System(GPS), cellular network locating technologies, and WI-FI based locatingtechnologies, and other technologies. One example of an LBS is alocation based messaging services wherein notifications and othermessages to users can be in dependence on the respective locations ofthe users.

Data structures have been employed for improving operation of computersystem. A data structure refers to an organization of data in a computerenvironment for improved computer system operation. Data structure typesinclude containers, lists, stacks, queues, tables and graphs. Datastructures have been employed for improved computer system operatione.g. in terms of algorithm efficiency, memory usage efficiency,maintainability, and reliability.

Artificial intelligence (AI) refers to intelligence exhibited bymachines. Artificial intelligence (AI) research includes search andmathematical optimization, neural networks and probability. Artificialintelligence (AI) solutions involve features derived from research in avariety of different science and technology disciplines ranging fromcomputer science, mathematics, psychology, linguistics, statistics, andneuroscience. Machine learning has been described as the field of studythat gives computers the ability to learn without being explicitlyprogrammed.

SUMMARY

Shortcomings of the prior art are overcome, and additional advantagesare provided, through the provision, in one aspect, of a method. Themethod can include, for example: determining a current transportationmode of a user with use of data of a user passenger vehicle associatedto the user and data of a user auxiliary passenger vehicle associated tothe user, wherein the user passenger vehicle is capable of carrying theuser auxiliary vehicle, and wherein the user auxiliary vehicle isconfigured to be hand carried by the user; evaluating a current route ofthe user in dependence on the current transportation mode of the user asdetermined by the determining; and providing one or more output independence on the evaluating.

In another aspect, a computer program product can be provided. Thecomputer program product can include a computer readable storage mediumreadable by one or more processing circuit and storing instructions forexecution by one or more processor for performing a method. The methodcan include, for example: determining a current transportation mode of auser with use of data of a user passenger vehicle associated to the userand data of a user auxiliary passenger vehicle associated to the user,wherein the user passenger vehicle is capable of carrying the userauxiliary vehicle, and wherein the user auxiliary vehicle is configuredto be hand carried by the user; evaluating a current route of the userin dependence on the current transportation mode of the user asdetermined by the determining; and providing one or more output independence on the evaluating.

In a further aspect, a system can be provided. The system can include,for example a memory. In addition, the system can include one or moreprocessor in communication with the memory. Further, the system caninclude program instructions executable by the one or more processor viathe memory to perform a method. The method can include, for example:determining a current transportation mode of a user with use of data ofa user passenger vehicle associated to the user and data of a userauxiliary passenger vehicle associated to the user, wherein the userpassenger vehicle is capable of carrying the user auxiliary vehicle, andwherein the user auxiliary vehicle is configured to be hand carried bythe user; evaluating a current route of the user in dependence on thecurrent transportation mode of the user as determined by thedetermining; and providing one or more output in dependence on theevaluating.

Additional features are realized through the techniques set forthherein. Other embodiments and aspects, including but not limited tomethods, computer program product and system, are described in detailherein and are considered a part of the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more aspects of the present invention are particularly pointedout and distinctly claimed as examples in the claims at the conclusionof the specification. The foregoing and other objects, features, andadvantages of the invention are apparent from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 is a block diagram of a system having a manager system, vehiclesystems, associated user auxiliary vehicle systems, client computerdevices, an IoT sensor system, a weather system, a geo classificationsystem, a ground traffic surface system, a public transportation servicesystem and a social media system according one embodiment;

FIG. 2 is a flowchart illustrating performance of a method by a managersystem according to one embodiment;

FIG. 3 is a schematic block diagram depicting physical attributes of asystem having a manager system, a vehicle system, a user auxiliaryvehicle system, and a client computer device according to oneembodiment;

FIGS. 4A and 4B is a flowchart depicting a method for performance by amanager system interoperating with the vehicle system, a user auxiliaryvehicle system, a client computer device, and various other systemsaccording to one embodiment;

FIG. 4C depicts a method for performance by a manager system accordingto one embodiment;

FIG. 5 depicts a user interface display usable by a user and displayedon a client computer device of a user and/or on a display of a vehiclesystem and/or a user auxiliary vehicle system associated to a useraccording to one embodiment;

FIG. 6A is a geospatial map illustrating a route and a trip forperformance by user in one or more of vehicle passenger transportationmode or user auxiliary vehicle transportation mode, according to oneembodiment;

FIG. 6B is an exploded geospatial map highlighting a portion of thegeospatial map of FIG. 6A depicting features in respect to splitting ofa trip between user passenger vehicle transportation mode and userauxiliary vehicle transportation mode according to one embodiment;

FIG. 6C is an exploded view highlighting portion of the geospatial mapof FIG. 6A depicting features in relation to iteratively updating acurrent route of a user when traveling in a user auxiliary vehicletransportation mode according to one embodiment;

FIG. 7A is a predictive model for return of predictions in respect toaverage speeds of the vehicles traveling through a geospatial region oralong the route segment according to one embodiment;

FIG. 7B is a predictive model for return of predictions as to openparking spaces within geospatial region according to one embodiment;

FIG. 8 depicts a computing node according to one embodiment;

FIG. 9 depicts a cloud computing environment according to oneembodiment; and

FIG. 10 depicts abstraction model layers according to one embodiment.

DETAILED DESCRIPTION

System 100 for determining and implementing transportation vehicleroutes is shown in FIG. 1 . System 100 can include manager system 110,having associated data repository 108, vehicle systems 120A-120Z, userauxiliary vehicle systems 121A-121Z client computer devices 130A-130Z,IoT sensor system 140, weather service system 150, geo classificationsystem 160, ground traffic services system 170, public transportationservice system 172, and social media system 180. Manager system 110,vehicle systems 120A-120Z, user auxiliary vehicle systems 121A-121Z,client computer devices 130A-130Z, IoT sensor system 140, geo weatherservice system 150, classification system 160, ground traffic servicessystem 170, public transportation service system 172, and social mediasystem 180 can be computing node based systems and devices incommunication with one another via network 190. Network 190 can be aphysical network and/or virtual network. A physical network can includefor example a physical telecommunications network connecting numerouscomputing nodes such as computer servers and computer clients. A virtualnetwork can, for example, combine numerous physical networks, or partsthereof into a logical virtual network. In another example, numerousvirtual networks can be defined over a single physical network.

According to one embodiment, manager system 110 can be external tovehicle systems 120A-120Z, user auxiliary vehicle systems 121A-121Z,client computer devices 130A-130Z, IoT sensor system 140, weatherservice system 150, geo classification system 160, ground trafficservice system 170, public transportation service system 172, and socialmedia system 180. According to one embodiment, manager system 110 can beco-located with one or more vehicle systems 120A-120Z, user auxiliaryvehicle systems 121A-121Z, client computer devices 130A-130Z, IoT sensorsystem 140, geo classification system 160, ground traffic service system170, weather service system 150, public transportation service system172, and social media system 180.

Vehicles systems 120A-120Z can be of and physically affixed torespective vehicles such as user passenger vehicles as may be providedby e.g. cars and trucks. User auxiliary vehicle systems 121A-121Z can beof and physically affixed to user auxiliary vehicles. User auxiliaryvehicles herein can be personal vehicles that can be motorized orunmotorized. Examples of a motorized transportation vehicle can be amotorized scooter. An example of an unmotorized personal transportationvehicle can be a bicycle. User auxiliary vehicles associated torespective vehicle systems 120A-120Z can be configured to behand-carried by a user. In another aspect a user passenger vehicleherein can be configured to carry a user auxiliary vehicle such as apersonal vehicle. For example, the vehicle can be configured to carry auser auxiliary vehicle, e.g., by carrying the user auxiliary vehicle ina trunk thereof, cabin thereof, storage thereof, or flatbed thereof inthe case of a truck. A vehicle can be configured to carry a userauxiliary vehicle also or alternatively by way of a rack onto which theuser auxiliary vehicle can be removably attached by manual action of auser.

Each of the different client computer devices 130A-130Z can beassociated to a different user. Regarding one or more client computerdevice 130A-130Z, a computer device of one or more client computerdevice 130A-130Z in one embodiment can be a computing node deviceprovided by a client computer, e.g. a mobile device, e.g. a smartphoneor tablet, a laptop, smartwatch that runs one or more program, e.g.including a web browser for opening and viewing web pages. In someembodiments, client computer devices of client computer devices130A-130Z can include biometric sensors for output of biometric sensordata. Manager system 110 can receive biometric data output by such abiometric sensor.

IoT sensor system 140, according to one embodiment, can include aplurality of cameras disposed about a spatial geometrical area supportedby system 100. According to one embodiment, the cameras of IoT sensorsystem 140 can be configured to provide image data which can beprocessed to determine whether a parking spot is open or occupied. Inone embodiment, manager system 110 running image recognition process toexamine spatial image data representing a feature of interest (e.g. aparking spot available or closed status) can include manager system 110employing pattern recognition processing using one or more of e.g.feature extraction algorithms, classification algorithms, and/orclustering algorithms. In one embodiment, manager system 110 running animage recognition process can include performing of digital imageprocessing. Digital image processing can include, e.g., filtering, edgedetection, shape classification, optical character recognition (OCR),and/or encoded information decoding.

Geo classification system 160 can associate venues to spatial coordinateranges associated to the venues. Geo classification system 160 caninclude enhanced spatial maps pre-marked with tags to indicate usesassociated with map features. Map features can include venues havingassociated venue areas including venue building areas and/or associatedvenue parking areas. An enhanced map can include tag specifying usageclassifications of venues, e.g. residential, business, public and thelike. An enhanced map can be tagged to tag features of a map includinge.g. roads, bodies of water and infrastructure features such as buildingwalls (exterior and interior) defining enterprise venues. In some cases,an enhanced map can be tagged so that that different parts of a buildingare tagged differently, e.g. a doorway can be tagged differently from aconference room. Geo classification system 160 can provide data tagsassociated to locations that specify uses associated with variouslocations. Geo classification system 160 can cognitively map venuesidentified by venue identifiers, e.g. names, addresses, classifications,and the like, to coordinate location ranges associated to the variousvenues. Accordingly, manager system 110 querying geo classificationsystem 160, with location data in the form of coordinate location data,can return an identifier venue. Further, geo classification system 160can cognitively map venues that are listed in geo-classification systemwith uses associated with such venues, e.g. whether such venues are forresidential use or business use and include a variety of additional oralternative use classifications, e.g. public use, roadway use, waterwayuse, and the like. An enhanced map can be tagged to tag features of amap, including e.g. roads, bodies of water and venues. Venues can beprovided e.g. by open spaces, such as developed open spaces orundeveloped open spaces, buildings such as open roof building or closedroof buildings, and/or open spaces associated to buildings, such asbuilding associated with a parking lot. For each venue, geoclassification system 160 can associate e.g. identifiers for the venue,ranges and coordinate locations associated with the venue, featuresassociated to the venue, such as building infrastructure features,parking lot features, and other features. Geo classification system 160according to one embodiment can be provided by GOOGLE MAPS® (GOOGLEMAPS® is a registered trademark of Google, Inc.).

Weather service system 150 can be configured to provide weather datawith respect to an area being serviced by system 100. Weather data caninclude e.g. historical temperature data, precipitation data, wind dataand weather event data. Weather data can include e.g. currenttemperature data, precipitation data, wind data and weather event data.Weather data can include e.g. forecast temperature data, precipitationdata, wind data and weather event data. Weather events can include e.g.storms including hurricanes, tornados, fog formations, heat waves andcold waves. Weather service system 150 can store weather data associatedto different subareas of an area being serviced by system 100.

Ground traffic services system 170 can be configured to providealternate candidate vehicle driving routes between first and secondlocations. Ground traffic services system 170 can be configured toprovide a selected optimum driving route between first and secondlocations. Ground traffic services system 170 can be configured toprovide data on traffic conditions such as current traffic conditionsover a geospatial area. Ground traffic services system 170, accordingone embodiment can be collocated with geo classification system 160.

Public transportation service system 172 can provide data on schedulesfor public transportation, e.g., trains, buses, and the like. Publictransportation service system 172 can provide real-time data that isupdated based on real-time changes from publicly published schedules forarrival times and departure times.

Social media system 180 can include a collection of files, including forexample, HTML files, CSS files, image files, and JavaScript files.Social media system 180 can be a social website such as FACEBOOK®(Facebook is a registered trademark of Facebook, Inc.), TWITTER®(Twitter is a registered trademark of Twitter, Inc.), LINKEDIN®(LinkedIn is a registered trademark of LinkedIn Corporation), orINSTAGRAM® (Instagram is a registered trademark of Instagram, LLC).Computer implemented social networks incorporate messaging systems thatare capable of receiving and transmitting messages to client computersof participant users of the messaging systems. Messaging systems canalso be incorporated in systems that that have minimal or no socialnetwork attributes. A messaging system can be provided by a shortmessage system (SMS) text message delivery service of a mobile phonecellular network provider, or an email delivery system. Manager system110 can include a messaging system in one embodiment. During a processof registration wherein a user of system 100 registers as a registereduser of system 100, a user sending registration data can send withpermission data defining the registration data a permission that grantsaccess by manager system 110 to data of the user within social mediasystem 180. On being registered, manager system 110 can examine data ofsocial media system 180 e.g. to determine whether first and second usersare in communication with one another via a messaging system of socialmedia system 180. A user can enter registration data using a userinterface displayed on a client computer device of client computerdevices 130A-130Z. Entered registration data can include e.g. name,address, social media account information, other contact information,biographical information, background information, preferencesinformation, and/or permissions data e.g. can include permissions dataallowing manager system 110 to query data of a social media account of auser provided by social media system 180 including messaging system dataand any other data of the user. When a user opts-in to register intosystem 100 and grants system 100 permission to access data of socialmedia system 180, system 100 can inform the user as to what data iscollected and why, that any collected personal data may be encrypted,that the user can opt out at any time, and that if the user opts out,any personal data of the user is deleted.

Data repository 108 in roadmaps area 2121 can store data on userpassenger vehicle roads traversable within the geospatial area servicedby system 100. Roadmaps area 2121 can include such data as geospatialcoordinates defining a roadway, as well as other data tags such as roadmaintenance data, road attributes e.g. shoulder, no shoulder, associatedguardrail, associated act accident records, and the like.

Data repository 108 in trail maps area 2122 can store data on trailsthat are accessible by user auxiliary vehicles such as personal vehiclesthat are not accessible by user passenger vehicles. Roadmaps area 2121can store data on roads accessible by user passenger vehicles and trailmaps area 2122 can store data on trails accessible by user auxiliaryvehicles but not accessible by user passenger vehicles. Manager system110 can be configured to build up data of trail maps area 2122heuristically over time by examining historical routes of users overtime who are traveling by riding of personal vehicles through ageospatial area being serviced by system 100. Manager system 110, forexample, can be configured to log into trail maps area 2122, a new trailafter observing a new trail for certain personal vehicle type. Afterexamining path data of user auxiliary vehicles for certain typeindicates that a certain path has been used as a trail more than N timesby users of system 100 riding in certainly classified vehicle type.

Data repository 108 in users area 2123 can store data on users of system100. Users of system 100 can be registered users of system 100. Onreceipt of registration data from a user manager system 110 canestablish a Universally Unique Identifier (UUID) for the user and canstore a record for that user into users area 2123. Manager system 110can be configured so that on registration more than one vehicle can beassociated to a user in response to user defined registration data. Forexample, on registration, a user can define data specifying that acertain vehicle provided by user passenger vehicle is associated to theuser. A user on registration can also specify that a certain userauxiliary vehicle is associated to the user. Therefore, on such aregistration the vehicle and the user auxiliary vehicle associated withthe user can be associated to each other by having a commonly associateduser. Within users area 2123, there can be stored for a given user,e.g., a UUID and identifiers for user passenger vehicles and userauxiliary vehicles associated to such users.

Data repository 108 in vehicles area 2124 can store data on userpassenger vehicles supported by system 100. In vehicles area 2124, datarepository 108 can store data on user passenger vehicles such as carsand trucks that are associated to respective users of system 100.

Data repository 108 in user auxiliary vehicles area 2125 can store dataon user auxiliary vehicles supported by system 100. In user auxiliaryvehicles area 2125 data repository 108 can store data, e.g. respectingmotorized and unmotorized human powered personal vehicles that areassociated respective users of system 100. Motorized vehicles supportedby system 100 can include e.g. motorized scooters, motorized sleds.Unmotorized vehicles that can be supported by system 100 can includee.g. bicycles.

Data repository of 108 in routes history area 2126 can store data onhistorical routes traveled by vehicles supported by system 100. Datarepository 108 in routes history area 2126 can store data on historicalroutes traveled by user passenger vehicles as well as user auxiliaryvehicles supported by system 100. The route data stored in routeshistory area 2126 can be broken on down into route segments.

Data repository 108 in user passenger vehicle parking area 2127 canstore data on user passenger vehicle parking spaces over the geospatialarea supported by system 100. Manager system 110 can assign the UUID toeach user passenger vehicle parking system recognized in a geospatialarea being serviced by system 100. Within vehicle parking area 2127there can be stored data specifying historical parking space occupiedtimes and parking space open times for respective user passenger vehicleparking spaces recognized by system 100.

Data repository 108 in user auxiliary vehicle parking area 2128 canstore data on parking areas for user auxiliary vehicles e.g. motorizedor unmotorized personal vehicles. Data repository 108 and user auxiliaryvehicle parking area 2128 can store data that specifies parking spaceopen and parking space occupied periods for respective auxiliary parkingspaces supported by system 100 over time. The user auxiliary vehicleparking spaces specified user auxiliary vehicle parking area 2128 caninclude tags specifying user auxiliary vehicle type, e.g., motorizedscooter, motorized sled, human powered bicycle, and the like.Embodiments herein recognize that some user auxiliary vehicle types canbe compact enough so as not to require an associated parking space e.g.can be hand carried by a user, perhaps folded, in some embodiments intoa building associated to the final destination e.g. a retail store,office building, recreation venue, and the like.

Data repository 108 in decision data area can store various decisiondata structure and predictive models for use in return of actiondecision. Decision data structures can include e.g. decision tables anddecision. Predictive models can include predictive models trained bymachine learning processes using historical data collected within datarepository 108.

Manager system 110 can run various processes. Manager system 110 runningpreparation and maintenance process 111 can prepare data for storageinto storage into various areas 2121-2129 of data repository 108 and canstore and maintain data in the various areas 2121-2129 of datarepository 108. Manager system 110 running preparation maintenanceprocess 111 can run various processes to innovatively query various datasources for return data. The various data sources can include e.g.vehicle systems 120A-120Z, user auxiliary vehicle systems 121A-121Z,client computer devices 130A-130Z, IoT sensor system 140, weather system150, geo spatial system 160, ground traffic services system 170, publictransportation service system 172, and social media system 180. Managersystem 110 running preparation and maintenance process 111 can includemanager system 110 running various processes to structuralizedunstructured data into structured form prior to storage into one or morearea of areas 2121-2129 of data repository 108.

Manager system 110 running traveling mode detection process 112 caninclude manager system 110 detecting current mode of transportation of auser. Manager system 110 running traveling mode detection process 112can store path data, i.e. location over time data of (a) user passengervehicle and (b) a user auxiliary vehicle of a user over time. Managersystem 110 on determining based on the examining that the user passengervehicle and the user auxiliary vehicle are moving together over time candetermine that the user is traveling in a user passenger vehicletraveling mode. Manager system 110, based on a determining that a userpassenger vehicle of the user is parked, and a user auxiliary vehicle ofthe user is moving, can determine that the user is currently moving in auser auxiliary vehicle transportation mode.

Manager system 110 running transportation route determination process113 can include manager system 110 determining a current route for acurrent trip. Manager system 110 miming transportation routedetermination process 113 can include manager system 110 iterativelyupdating a current route for a current trip. Manager system 110 runningtransportation route determination process 113 can include managersystem 110 in some cases determining that an optimal route for a currenttrip is a multimode transportation route that comprises more than onetransportation mode, e.g., a first transportation mode provided by auser passenger vehicle transportation mode and a second transportationmode provided by a user auxiliary vehicle transportation mode.

Manager system 110 running transportation route determination process113 can examine data of geospatial regions which can be referred to ascells, and for selected cells can evaluate a predicted performance of auser passenger vehicle mode and a predicted performance of a userauxiliary vehicle transportation mode. On the determination that one ormore destination cell of the current route has a predicted userauxiliary vehicle transportation mode performance that exceeds predictedperformance for a user passenger vehicle transportation mode managersystem 110 can establish a multimode transportation route as the currentroute for a current trip.

Manager system 110 running route evaluation process 114 can evaluatedifferent candidate routes, such as different user auxiliary vehicletransportation routes. Manager system 110 running route evaluationprocess 114 can include manager system 110 applying a multi-factorcriterion for evaluation. The multiple factors can include e.g. adestination time factor, a comfort factor, a physical exhaustion factor(e.g. in the case of a human powered user auxiliary vehicle), and acurrent route factor (e.g. so that a current route is given priorityover a new route).

Manager system 110 can run NLP process 113 to process data forpreparation of records that are stored in data repository 108 and forother purposes. Manager system 110 can run a Natural Language Processing(NLP) process 115 for determining one or more NLP output parameter of amessage. NLP process 115 can include one or more of a topicclassification process that determines topics of messages and output oneor more topic NLP output parameter, a sentiment analysis process whichdetermines sentiment parameter for a message, e.g. polar sentiment NLPoutput parameters, “negative,” “positive,” and/or non-polar NLP outputsentiment parameters, e.g. “anger,” “disgust,” “fear,” “joy,” and/or“sadness” or other classification process for output of one or moreother NLP output parameters e.g. one of more “social tendency” NLPoutput parameter or one or more “writing style” NLP output parameter.

By running of NLP process 115 manager system 110 can perform a number ofprocesses including one or more of (a) topic classification and outputof one or more topic NLP output parameter for a received message (b)sentiment classification and output of one or more sentiment NLP outputparameter for a received message or (c) other NLP classifications andoutput of one or more other NLP output parameter for the receivedmessage.

Topic analysis for topic classification and output of NLP outputparameters can include topic segmentation to identify several topicswithin a message. Topic analysis can apply a variety of technologiese.g. one or more of Hidden Markov model (HMM), artificial chains,passage similarities using word co-occurrence, topic modeling, orclustering. Sentiment analysis for sentiment classification and outputof one or more sentiment NLP parameter can determine the attitude of aspeaker or a writer with respect to some topic or the overall contextualpolarity of a document. The attitude may be the author's judgment orevaluation, affective state (the emotional state of the author whenwriting), or the intended emotional communication (emotional effect theauthor wishes to have on the reader). In one embodiment sentimentanalysis can classify the polarity of a given text as to whether anexpressed opinion is positive, negative, or neutral. Advanced sentimentclassification can classify beyond a polarity of a given text. Advancedsentiment classification can classify emotional states as sentimentclassifications. Sentiment classifications can include theclassification of “anger,” “disgust,” “fear,” “joy,” and “sadness.”

Manager system 110 running NLP process 115 can include manager system110 returning NLP output parameters in addition to those specificationtopic and sentiment, e.g. can provide sentence segmentation tags, andpart of speech tags. Manager system 110 can use sentence segmentationparameters to determine e.g. that an action topic and an entity topicare referenced in a common sentence for example.

The flowchart of FIG. 2 illustrates a process for performance by managersystem 110 associated to an enterprise, such as an online retailer. FIG.2 is a flowchart illustrating coordination of processes that can beperformed by manager system 110 of FIG. 1 in accordance with one or moreembodiment set forth herein.

At block 210, manager system 110 can run preparation and maintenanceprocess 111 to populate prepare and maintain various data of datarepository 108 including data of areas 2121-2129. Manager system 110 canrun preparation and maintenance process 111 iteratively until process111 is terminated at block 212.

At block 220, Manager system 110 can run route evaluation process 114.For support of running of route evaluation process 114 iteratively,manager system 110 can be running e.g. mode detection process 112,multimode transportation route determination process 113, and/or NLPprocess 115. Manager system 110 can run route evaluation process 114until route evaluation process 114 is terminated at block 222. Managersystem 110 can run preparation and maintenance process 111 and routeevaluation process 114 concurrently and can run each of process 111 andprocess 114 iteratively.

A physical schematic view of system 100 is shown in FIG. 3 . The leftside of FIG. 3 depicts a user passenger vehicle transportation mode inwhich user 129A travels within vehicle 1200 provided by a user passengervehicle in a user passenger vehicle transportation mode. The right handside of FIG. 3 depicts an auxiliary transportation mode in which user129A travels on user auxiliary vehicle 1210 in a user auxiliary vehicletransportation mode. In FIG. 3 user auxiliary vehicle 1210 as shown asbeing provided by a motorized scooter and another use case userauxiliary vehicle 1210 can be provided by another form of motorizedpersonal vehicle. In one embodiment, user auxiliary vehicle 1210 can beprovided by manually powered personal vehicle such as a bicycle. In theembodiment of FIG. 3 . The auxiliary transportation mode 1210 is shownas being provided by an auxiliary transportation mode in which user 129Arides on a personal vehicle. In another example of an auxiliarytransportation mode user 129A which can be referred to as a publicvehicle transportation mode user 129A can travel on a publictransportation vehicle, e.g. a publicly available train or bus.

Manager system 110 running transportation mode detection process 112 candetect current mode of transportation of a user. Manager system 110 canbe iteratively obtaining location data of vehicle system 120A which canbe physically affixed to vehicle 1200 provided by user passengervehicle, user auxiliary vehicle system 121A which can be physicallyaffixed to user auxiliary vehicle 1210 (e.g. disposed in a handleconsole as shown) and client computer system 130A can be substantiallycontinually physically associated to user 129, at all times. Accordingto one embodiment, vehicle system 120A, user auxiliary vehicle system121A, and client computer device 130A can have incorporated therein arespective GPS sensor that senses and processes radiofrequency signalsfrom orbiting satellites and based on the processing of such signals canreport current coordinate location of the respective vehicle system120A, user auxiliary vehicle system 121A, or client computer device130A.

System 100 can be configured alternatively or additionally so thatmanager system 110 iteratively receives location data of vehicle system120A, user auxiliary vehicle system 121A, and/or client computer device130A from a locating service that provides location data of vehiclesystem 120A, user auxiliary vehicle system 121A, and client computerdevice 130A by processing radiofrequency signals received from e.g.cellular service and/or wireless LAN e.g. Wi-Fi networks. Location dataof vehicle system 120A, user auxiliary vehicle system 121A, and clientcomputer device 130A collected over time into data repository 108defines path data. Manager system 110 running transportation modedetection process 112 can determine the current transportation mode ofuser 129A by way of examining path data. Where vehicle system 120A anduser auxiliary vehicle system 121A travel together manager system 110can determine that user 129A is traveling in a user passenger vehicletransportation mode. Where user auxiliary vehicle system 121A istraveling along a path and vehicle system 120A is not moving managersystem 110 can determine that user 129A is traveling in a user auxiliaryvehicle transportation mode, i.e. is being transported by user auxiliaryvehicle 1210. Manager system 110 can store path data for user passengervehicles in vehicles area 2124, can store path data for user auxiliaryvehicles into auxiliary vehicles area 2125, and can store path data forclient computer devices (users) into users area 2123.

Flowchart of FIGS. 4A and 4B depict a method for performance managersystem 110 interoperating with vehicle system 120A, user auxiliaryvehicle system 121A, client computer device 130A, and systems 140, 150,160, 170, 172, and 180.

At block 1101 manager system 110 can be receiving registration data fromclient computer device 130A. Client computer device 130A can beiteratively sending registration data at block 1301. A user can defineregistration data using registration area 5002 of user interface 5000depicted in FIG. 5 . User interface 5000 can be a displayed userinterface for display on a display of client computer device of clientcomputer devices 130A-130Z, such as client computer device 130A of user129A (FIG. 3 ).

Registration data defined using registration area 5002 can include e.g.contacts data, permissions data, vehicles data, and preferences data. Auser can enter contact data using area 5004 of registration area 5002.Contact data can include e.g. name, address, phone number, social mediaaccount data, and email address data. Permissions data can be definedusing area 5006 of registration area 5002. Permissions data can includepermissions specifying that manager system 110 can use e.g. locationdata of the user, including vehicles of the user, and social media dataof the user. Vehicles data can be defined using area 5008 ofregistration area 5002. In vehicles area 5008 a user can enter dataspecifying vehicles associated to user. Vehicles associated to user caninclude e.g. user passenger vehicles and/or personal vehicles as setforth herein. A user can define preferences data using preferences area5010 of registration area 5002. Preferences defined using preferencesarea 5010 can include e.g., air temperature preferences of a user andair conditioned preferences of a user. In response to the receipt ofregistration data at block 1101 manager system 110 can proceed to block1102.

At block 1102, manager system 110 can send received registration datafor receipt and storage by data repository 108 at block 1081. Receivedregistration data can be stored in users area 2123 of data repository108. In response to completion of block 1102 manager system 110 canestablish an account for the user sending registration data at block1301. Manager system 110 can assign a UUID for each new user andadditional UUIDs for each vehicle including each user passenger vehicleand each user auxiliary vehicle provided by personal vehicle specifiedin registration data by a user on registration. In response tocompletion of block 1102 manager system 110 can proceed to block 1103.Manager system 110 can assign UUIDs to vehicles with reserved charactersfor referencing a vehicle type. Manager system 110 can determine avehicle type based on registration data defined by a user to specify thevehicle type or can examine location data or other characterizing datafrom the vehicle systems discern vehicle type based on movement or otherbehavior characteristics of the vehicle systems.

At block 1103, manager system 110 can send installation packages forreceipt by vehicle system 120A at block 1201, user auxiliary vehiclesystem 121A at block 1211, and client computer device 130A at block1302. In response to the receipt of an installation package at block1201 vehicle system 120A at block 1201 can install the installationpackage. In response to receipt of the installation package at block1211 user auxiliary vehicle system 121A can install the installationpackage. In response to the receipt of the installation package at block1302 client computer device 130A can install the installation package.Installation packages for receipt and installation of blocks 1201, 1211,and 1302 can include e.g. libraries and executable code that adapt andoptimize vehicle system 120A, user auxiliary vehicle system 121A, andclient computer device 130A for use in system 100. Installation packagesfor receipt and installation of blocks 1201, 1211, and 1302 can includee.g. UUIDs e.g. UUIDs for user passenger vehicles, user auxiliaryvehicles, and users respectively so that vehicle system 120A, auxiliaryvehicle system 121A and client computer device 130A respectively tagsent data for later sending to manager system 110 so that that managersystem 110 can discern the source of the sent data. Assigned UUIDs foruser passenger vehicles and user auxiliary vehicles can include reservedcharacter sections that specify the vehicle type (e.g. user passengervehicle, user auxiliary vehicle/motorized scooter, etc.). Depending onfeaturization, the installation packages can be, e.g. thick clientinstallation packages or thin client installation packages. In responseto completion of block 1103 manager system 110 can proceed to block1104.

At block 1104, manager system 110 can be receiving route request datafrom client computer device 130A. Client computer device 130A can besending route requests data at block 1303 iteratively. The user candefine route request data using route request area 5014 of userinterface 5000 as shown in FIG. 5 . A route request can include aspecified route starting point and a route destination. A route startingpoint can be expressly specified by a user or, in the absence of anexpressly specified starting location, can be entered by default as thecurrent location of client computer device 130A.

At block 1104 manager system 110 can alternatively or additionally bereceiving route request data defined by the user, such as user 129Ausing an associated user interface of vehicle system 120A and/or userauxiliary vehicle system 121A. In response to completion of block 1104manager system 110 can proceed to block 1105 to send query data tovehicle system 120A for receipt of location data of vehicle system 120A.In response to completion of block 1104 manager system 110 can proceedto blocks 1105, 1108, 1111, and 1114 to send query data.

According to one embodiment, route request data defined by a user canspecify a trip from a starting location to a destination location. Asset forth herein, manager system 110 can generate a selected route inresponse to received route request data, which route request data canspecify a trip of a user from a starting location to a destinationlocation. Manager system 110 can be configured so that manager system110 can iteratively update a selected route while the user performs thetrip and travels to the destination location. As part of iterativelyupdating a route, manager system 110 can generate a multimodetransportation route as the current selected route for the trip.

Query data sent at block 1105 can include query data to query currentlocation of vehicle system 120A. Query data sent at block 1108 caninclude query data to determine current location of user auxiliaryvehicle system 121A. Query data sent at block 1111 can include querydata to query a current location of client computer device 130A. Querydata sent at block 1114 can include query data to query various datafrom systems 140, 150, 160, 170, 172 and/or 180. Vehicle system 120A canreceive the query data sent at block 1105 at block 1202 and can respondto the received query data at send block 1203. User auxiliary vehiclesystem 121A can receive the query data sent from manager system 110 atblock 1108 at block 1212 and can respond to the query data at send block1213. Client computer device 130A can receive the query data sent bymanager system 110 at block 1111 at block 1304 and can respond to thequery data at send block 1305. Systems 140, 150, 160, 170, 172, and/or180 can receive the query data sent at block 1114 at block 1401 and canrespond the query data at send block 1402. Manager system 110 canreceive response data from vehicle system 120A at block 1106, canreceive response data from user auxiliary vehicle system 121A at block1109, can receive response data from client computer device 130A atblock, 1112, and can receive response data from systems 140, 150, 160,170, 172, and/or 180 at receive block 1115.

Manager system 110 can send received response data received from vehiclesystem 120A at block 1107 for receipt and storage by data repository 108at block 1082. Manager system 110 at block 1110 can send receivedresponse data received from user auxiliary vehicle system 121A at block1107. Manager system 110 can send received response data received atblock 1106 to data repository 108 for receipt and storage by datarepository 108 at block 1082. Manager system at block 1110 can sendreceived response data received at block 1109 for receipt and stored bydata repository 108 at block 1083. Manager system 110, at block 1113 cansend received response data received at block 1112 for receipt andstorage by data repository 108 at block 1084. Manager system 110 atblock 1116 can send response data received at block 1115 for receipt andstorage by data repository 108 at block 1085. Response data received bymanager system 110 at block 1106 can include location data specifying acurrent location of vehicle system 120A. Received response data receivedby manager system 110 at block 1109 can include location data specifyinga current location of user auxiliary vehicle system 121A. Receivedresponse data received by manager system 110 at block 1112 can includelocation data specifying a current location of client computer device130A. Received response data received by manager system 11 at block 1115can include e.g. IoT data of IoT sensor system 140, weather data fromweather system 150, geo classification data received from geoclassification system 160, route data and traffic data from groundtraffic service system 170, public transportation data from publictransportation service system 172, and social media data from socialmedia system 180. In response to completion of block 1116 manager system110 can proceed to block 1117 to generate a current route based on theroute request data received block 1104.

Manager system 110 performing block 1117 can include manager system 110performing multiple queries on data repository 108 as indicated by datarepository 108 performing query receive and respond block 1086. Managersystem 110 performing generate block 1117 can include manager system 110activating transportation mode detection process 112, multimodetransportation route determination process 113, and/or route evaluationprocess 114 (FIG. 1 ). Manager system 110 performing generate block 1117can include manager system 110 performing the process as set forth inFIG. 4C, according to one embodiment. By iteratively storing locationdata of vehicle system 120A, user auxiliary vehicle system 121A, andclient computer device 130A, manager system 110 can store data definingpath data of vehicle system 120A, user auxiliary vehicle system 121A,and client computer device 130A. Path data of a system or device can bedefined by location data of the system or device over time. With thepath data stored manager system 110 can examine the path data todetermine for any given time period whether any first and second vehicleare traveling together or not traveling together.

At block 1701 manager system 110 can determine whether user auxiliaryvehicle system 121A is traveling together with vehicle system 120A. Ifmanager system 110 at block 1701 determines that user auxiliary vehiclesystem 121A is traveling together with vehicle system 120A, managersystem 110 can determine that a current transportation mode of user 129Ais a user passenger vehicle transportation mode, and manager system 110can proceed to block 1702.

At block 1702, manager system 110 can set a baseline route for thevehicle associated to vehicle system 120A. For setting of a baselineroute, according to one embodiment, manager system 110 can query groundtraffic services system 170 for an optimum user passenger vehicle route,which route can be determined based on current traffic conditions. Forexample, referring to the geospatial map as depicted in FIG. 6A a returnroute can be the route depicted from starting location A to destinationlocation Z and can be defined by route 6002 which specifies a sequence,e.g., a sequence of roadway turns to be performed by vehicle 1200, whichis currently at start location A in response to completion of block1702, manager system 110 can proceed to block 1703. Referring to thegeospatial map of FIG. 6A, there are depicted parking spaces e.g.passenger vehicle parking spaces 6004 (circles) and auxiliary vehicleparking spaces (squares).

At block 1703 manager system 110 can examine cells in dependence on thedetermined baseline route determined at block 1702. Cells can begeospatial regions of the geospatial area supported by system 100. Cellscan be predetermined geospatial regions. In the example of thegeospatial spatial map of FIG. 6A, there are depicted 24 cells namelycell A1-F4. The cells depicted in FIG. 6A, are all square shaped and ofa common size. However, according to alternative embodiments, the cellscan be of different shapes e.g. rectangular, hexagonal, and thedifferent cells can have different shapes and/or different sizes. Forperforming examining of cells at block 1703, according to oneembodiment, manager system 110 can examine cells of the route determinedat block 1702. The cells encompassing route 6002, in the example of FIG.6A can include cell F2, cell E2, cell D2, cell C2, cell B2, cell B3,cell A3, and cell A4. Manager system 110 examining cells at block 1703can include manager system 110 evaluating a predicted user passengervehicle performance of the cell in comparison to predicted userauxiliary vehicle performance of the cell. The predicted user passengervehicle performance can be a prediction of the performance of a userpassenger vehicle through the cell and the predicted user auxiliaryvehicle performance of the cell can be the predicted performance of auser auxiliary vehicle through the cell.

Embodiments herein recognize that in some scenarios traveling throughgeospatial region via a personal vehicle as opposed to user passengervehicle can be advantageous. Accordingly, embodiments herein canestablish transportation routes that are multimode transportation routesincluding more than one transportation mode, e.g., can include a firstroute segment in which transportation is to be conducted according to auser passenger vehicle transportation mode and a second route segmentwherein transportation is to be conducted via a user auxiliary vehicletransportation mode. Embodiments herein recognize, for example, thatwhen user passenger vehicle becomes crowded, traveling throughgeospatial region in a user passenger vehicle can become frustrating toa user. While embodiments herein also recognize that when user passengervehicle traffic through a geospatial region becomes crowded, it maycontinue to be possible to travel through the geospatial region via auser auxiliary vehicle provided by personal vehicle without frustration.Embodiments herein can include scoring of a geospatial region based onpredicted performance of a user passenger vehicle through the geospatialregion in comparison to predicted performance of a user auxiliaryvehicle through the same geospatial region.

Manager system 110 performing examining of cells at block 1703 caninclude manager system 110 applying Eq. 1 as set forth herein forcomparing evaluating predicted performance of a user passenger vehiclethrough geospatial region in comparison to predicted performance of auser auxiliary vehicle through geospatial region.S=F ₁ W ₁ +F ₂ W ₂ +F ₃ W ₃ +F ₄ W ₄ +F ₅ W ₅  (Eq. 1)

Where S is the predicted score of a user passenger vehicle or a userauxiliary vehicle through the geospatial region, e.g., a cell, F₁ is anaverage speed factor, F₂ is a parking factor, F₃ is a comfort factor, F₄is a physical exhaustion factor, F₅ is a current route factor, and whereW₁-W₅ are weights associated to the various factors.

Manager system 110 performing examining of cells at block 1703 caninclude manager system 110 commencing the examination of cells beginningwith a final destination cell. Referring to FIG. 6A, a final destinationcell can be regarded to be to a cell that includes the destination of acurrent trip in a current route. Thus, in the example of FIG. 6A, afinal destination cell can be cell F₂ which encompasses destination Z.

Referring to Eq. 1 manager system 110 can use Eq. 1 to evaluatepredicted performance of a user passenger vehicle within a cell incomparison to predicted performance of a user auxiliary vehicle within acell. Thus, in evaluating a cell, manager system 110 can generate firstand second S values using Eq. 1. Manager system 110 can generate a firstS value S₁ where the first value S₁ is a value specifying the predictedperformance of a user passenger vehicle through a cell and can generatea second value S₂ where S₂ is the predicted performance of a userauxiliary vehicle through the cell.

Manager system 110, using Eq. 1 can assign a higher value under factorF₁, average speed, wherein the predicted average speed of a vehicle,e.g. passenger or auxiliary is predicted to be higher and can assign alower value under factor F₁ where a predicted average speed of apassenger or user auxiliary vehicle is predicted to be lower through acell.

For determining a predicted average speed of the vehicle through thecell, manager system 110 can query predictive model such as predictivemodel 7002 as shown in FIG. 7A. Predictive model 7002 can be trainedwith training data so as to be responsive to query data.

System 100 can be configured so that as routes are performed by vehiclesincluding user passenger vehicles and user auxiliary vehicles, managersystem 110 can apply training data to predictive model 7002 so thatpredictive model learns how factors influence performance of a vehiclethrough a set of locations. Manager system 110 can apply supervisedlearning training datasets to predictive model 7002. Training datasetscan include factors training data and results training data. Factorstraining data can include e.g., time classification data, locationclassification data, transportation mode data, and traffic density data.Results training data can include average speed data and can includee.g. time and/or distance and/or average speed (velocity) data. Averagespeed data applied as results training data can include data thatspecifies average speed and/or data from which average speed can bederived e.g., time and distance data.

Time classification data can include e.g. classification data thatclassifies a time during which a historical route was performed.Classification data can specify e.g. workday, weekend day, or holidayday. Time classification data can also specify an hour of the day inwhich route was performed. Location classification data can specify acell traversed during a historical route. Location classification datacan also or alternatively identify a segment of the route. For example,in respect to the geospatial map of FIG. 6A, each road segment of thedepicted roadways can be assigned UUID and also each route segmentdefined by a trail segment traversable by a user auxiliary vehicle canbe assigned to UUID. Transportation mode data can specify e.g. userpassenger vehicle transportation mode, user auxiliary vehicletransportation mode, and/or or a sub classifier that classifies as theclassification of a user auxiliary vehicle (user auxiliary vehicletransportation mode/motorized scooter). Weather condition data appliedas training data can include e.g. general weather condition data, e.g.specifying air temperature, sunny conditions, cloudy conditions, rainyconditions, snowy conditions, and the like. Weather condition dataapplied training data can also or alternatively include road conditiondata applied as training data e.g. icy, snow cover conditions, wet, andthe like. Manager system 110 can obtain weather condition data oncompletion of a route by a vehicle supported by system 100 by queryingof weather system 150 and/or ground traffic services system 170, forexample. Traffic density data applied as training data to predictivemodel 7002 can refer to a data value that characterizes traffic densityduring the route. Such data value can be obtained by querying e.g.ground traffic services system 170.

Predictive model 7002, once trained, can be able to respond to querydata. Query data applied to predictive model 7002 by manager system 110at block 1703 can include one or more of time classification data forthe current route at the final destinations, location classificationdata, e.g. cell ID for the final destination cell, transportation modeclassifier (for S₁, user passenger vehicle, for S₂, user auxiliaryvehicle transportation mode for the auxiliary vehicle being carried bythe user's passenger vehicle) current route weather condition data,i.e., the predicted weather conditions of the cell when encountered inthe future during performance of the current route, traffic density dataspecifying the expected predicted traffic density at the cell to beencountered at a predicted time according the baseline route. Managersystem can obtain the described query data during last iteration ofblock 1115 to receive data from one or more of systems 140, 150, 160,170, 172, and 180. In response to the one or more types of query datapredictive model 7002 can return output data. The output data usingpredictive model 7002 can be a predicted average speed data specifying apredicted average speed for a vehicle through the cell (a user passengervehicle for the S₁ calculation, a user auxiliary vehicle for the S₂calculation).

Manager system 110 can apply training data for training of predictivemodel 7002 on expiration of each successive predetermined time period ofinterest, e.g., each successive one hour time period of interestaccording to one example, or another selected time period.

Referring to geospatial map of FIG. 6A, a user passenger vehiclecarrying a user and a user auxiliary vehicle can be at location A duringan early iteration of block 1703. For early iterations of block 1703according to one example, predicted performance of a user passengervehicle transportation mode can exceed predicted performance of a userauxiliary vehicle transportation mode. For subsequent iterations ofblock 1703 according to one example (when a user moves away fromlocation A), predicted performance of a user auxiliary vehicletransportation mode might exceed predicted performance of a userpassenger vehicle transportation mode, at which point manager system 110can generate a multimode transportation route as the selected currentroute for a trip. Weather condition data, and traffic density dataapplied as query data to predictive model 7002 can be predicted future(referencing the time the user will reach the destination location usingthe baseline route) weather condition data, and traffic density data asmay be determined during most recent iteration of block 1115 to receivequery response data from systems 140, 150, 160, 170, 172, and 180.Manager system 110 can apply query data to predictive model 7002 toreturn average speed data and then apply the returned average speed datainto Eq. 1 for determination of S scores specifying predictedperformance of the user passenger vehicle and user auxiliary vehiclethrough a certain cell e.g., commencing with a final destination cellencompassing destination Z as shown in FIG. 6A.

Referring to factor F₂ of Eq. 1, factor F₂ can be a parking factor.Manager system 110 can assign a higher value according to factor F₂ whenparking is predicted to be more accessible and can assign lower valueunder factor F₂ when parking is predicted to be less accessible. Forpredicting parking availability manager system 110 can query predictivemodel 7004 as depicted in FIG. 7B. Predictive model 7004 can be trainedwith applied training data provided by historical data so as to learn arelationship between traffic density and parking availability. Each cellof geospatial area supported by system 100 can be modeled to predictparking availability with use of predictive model 7004. Training dataapplied to predictive model 7004 can include the factors labeledtraining data such as time classification data, cell classificationdata, traffic density data, and results labeled training data. Resultslabeled training data applied to predictive model 7004 can includeidentified open parking spaces which can have been identified by managersystem by processing of IoT data obtained from IoT sensor system 140.Different instances of predictive model 7004 can be trained, whereineach different instance can be associated to a different parking spacetype, e.g., parking space for user passenger vehicle, parking space foruser auxiliary vehicle of a certain user auxiliary vehicle subclassification.

The training dataset can be applied for training of predictive model7004 on completion of each successive predetermined time period ofinterest, e.g., each successive one hour time period of interestaccording to one example. A number of open parking spaces can bedetermined by examination of data returned from an IoT sensor system 140as may be determined by processing the received IoT data received duringthe last most recent iteration of block 1115.

Manager system 110 applying comfort factor F₃ can assign a higher valueunder factor F₃ where a predicted comfort level of the user whentraveling through a cell e.g. riding on a user passenger vehicle of theuser or a user auxiliary vehicle where the user is predicted to be morecomfortable and can assign a lower value under factor F₃ were the useris predicted to be less comfortable. In the case of a user traveling ina user passenger vehicle, according to one embodiment, a predictedcomfort level can be predicted, according to one example to not changesubstantially in dependence on weather conditions. However, in the caseof a user auxiliary vehicle provided by a personal vehicle predictedcomfort level of user can be in dependence on predicted weatherconditions when the user travels through the cell of a current trip andcurrent route at a predicted future time.

Regarding factor F₃ manager system 110 can apply a different comfortcriterion set comprising one or more criteria in dependence on a vehicletype associated to the future cell traversal. That is, manager system110 can use the decision data structure of Table A below for loading ofa certainly selected criterion set. Manager system 110 can detect anauxiliary vehicle type at block 1701 (FIG. 4C) when determining whethera user auxiliary vehicle of a user is traveling with a user passengervehicle of the user. Installation packages sent at block 1103 caninclude assigned UUIDs for user passenger vehicles, user auxiliaryvehicles, and client computer devices mapping to users. The UUIDs forvehicles can be provided to include reserved reference charactersspecifying vehicle type (e.g. with predetermined character stringsassociated to predetermined vehicle type). Received location datareceived from a vehicle system at block 1106 (from vehicle system 120A)and at block 1109 (from system) can be tagged with the assigned UUIDssent at block 1103. Accordingly, manager system 110 can identity avehicle type (user passenger vehicle, user auxiliary vehicle/motorizedvehicle etc.) by examination of received UUID data.

TABLE A Row Vehicle Type Criterion Set 1 User passenger vehicle A001 2User auxiliary vehicle - Motorized Scooter A002 3 User auxiliaryvehicle - Bicycle A003 . . . . . . . . .

As depicted in Table A manager system 110 can load a different criterionset in dependence on a vehicle type associated to the future traversalof a cell. Manager system 110 can iteratively adjust the criterion setsassociated with the different roles of Table A over time during thecourse of deployment of system 100. For example, over time, a user candefine different preference data specifying preferences of the user interms of preferred weather conditions using area 5010 of user interface5000 shown in FIG. 5 . Manager system 110 can iteratively query socialmedia system 180 to determine preferences of the user. Return data fromsocial media system 180 can be processed by manager system 110 byactivation of NLP process 115 to determine sentiments associated withtopics. Thus, if the user enters posts data into social media forms suchas “I don't like cycling when it's very sunny”, manager system 110 byactivation of NLP process 115 can extract a negative sentimentassociated to the topics “bicycling” and “sunny” so as to adjust thecriterion set A003 of Table A, in the manner that predicted sunnyconditions return lower comfort score in the case that a vehicle totraverse the cell at a future time is a bicycle.

Manager system 110 applying physical exhaustion factor F₄ can assign ahigher value under factor F₄ where the user is predicted to exhaustlimited physical energy during the performance during travel through thecell and can assign a lower value under factor F₄ where users predictedexpenditure of physical energy is expected to be greater. For applyingfactor F₄ manager system 110 can use the decision data structure such asTable B below.

TABLE B Row Vehicle Type Criterion Set 1 User passenger vehicle B001 2User auxiliary vehicle - Motorized Scooter B002 3 User auxiliaryvehicle - Bicycle B003 . . . . . . . . .

Referring to Table B, criterion set B003 associated with row three (userauxiliary vehicle—bicycle) can be established so that a lower score canbe returned under factor F₄, given that the user can be expected to beusing substantial physical energy using the specified user auxiliaryvehicle. Manager system 110 can assign higher values according to factorF₄ where cell traversal being evaluated is a second or Nth cell beingevaluated for traversal by user using a manually powered user auxiliaryvehicle. That is, manager system 110 can assign higher values underfactor F₄, where the user under a traversal scenario being evaluated isspecified to travel longer distances.

Referring to Table B manager system 110 can assign moderate values underfactor F₄ when Row 2 is fired, i.e. when the motorized scooter is theuser auxiliary vehicle and can assign highest values under factor F₄when the specified vehicle is a user passenger vehicle of the user (i.e.the lowest amount of physical energy expenditure).

Manager system 110 applying factor F₅ (current route factor) can assigna higher value where the higher value under factor F₅ increases thelikelihood of the current route being reselected and being unchanged andcan assign a lower value according to factor F₅, where a higher valueunder factor F₅ increases the likelihood of a changed route regardingfactor F₅. Embodiments herein recognize that continuing with the currentroute can provide advantages to a user, e.g., in terms of the user'sunderstanding of a current route, avoiding of confusion to a user. Are-selection of a current route can occur where an entirety of routesegments from a current location to a destination location were includedin a prior route.

In reference to the geospatial map of FIG. 6A, cell F2 can define afinal destination cell that encompasses a destination location Z.Manager system 110 can be configured so that if manager system 110 onevaluation of destination cell F2, determines that a user auxiliaryvehicle is predicted to outperform a user passenger vehicle through thedestination cell F2, manager system 110 can proceed to perform anevaluation as described in connection with Eq. 1 with respect to anadjacent destination cell. The adjacent destination cell can be a celladjacent to the final destination cell which encompasses the currentroute, which in the described example, is route 6002 as depicted in FIG.6A. Thus, in the described example of FIG. 6A cell E2 can be an adjacentdestination cell adjacent to destination cell F2. Manager system 110, atblock 1703 can proceed to iteratively evaluate adjacent destinationcells until, on evaluation of a cell, manager system 110 determines thata user passenger vehicle through an adjacent destination cell ispredicted using Eq. 1, to outperform a user auxiliary vehicle throughthe cell.

Manager system 110 at block 1703, in response to determining that thereis one or more destination cell for which a user auxiliary vehicle ispredicted to outperform a user passenger vehicle using Eq. 1 canestablish a multimode transportation route in which a firsttransportation mode, i.e. a user passenger vehicle transportation modecan be used for first route segment of a trip and a secondtransportation mode, i.e. a user auxiliary vehicle transportation modecan be used for second route segment of the trip. Route segments hereincan include one or more sub segments. On performance of examining cellsblock 1703 manager system 110 can proceed to block 1704.

At block 1704 manager system 110 can determine whether there are Nadjacent destination cells satisfying the criterion. The criterion canbe that N adjacent cell destination cells satisfy the criterion whereina predicted performance of a user auxiliary vehicle through the Nadjacent cells exceeds the predicted performance of a user passengervehicle through the N adjacent cells. System 100 can be configuredaccording to one embodiment so that under light traffic conditions,there is less likelihood using Eq. 1 of user auxiliary vehicle beingpredicted to outperform a user passenger vehicle through a cell.Accordingly, under light traffic conditions, e.g., on a weekend morningmanager system 110 can be expected to establish as a route for a currenttrip, a single mode route, i.e., a route that is of the single modetransportation mode, i.e. a user passenger vehicle transportation modeall the way from the start location A to destination location Z. It canbe seen, however, according to Eq. 1 that system 100 can be configuredso that in the case of heavier traffic conditions use of Eq. 1 is morelikely to return the determination that a user auxiliary vehicle ispredicted to outperform a user passenger vehicle through a cell.Accordingly, in the case of heavier traffic conditions and with otherconditions as expressed in Eq. 1 being present, manager system 110 withuse of Eq. 1 can return the decision to establish as a route for acurrent trip, a multimode transportation route in which a first routesegment is performed with use of a user passenger vehicle transportationmode and a second route segment of the trip is performed with use of auser auxiliary vehicle transportation mode. In another aspect, managersystem 110 can be configured to dynamically adjust a current route for acurrent trip based on changes as determined by processing of iterativelyobtained data for processing.

Referring again to the flowchart of FIGS. 4A and 4B manager system 110can be iteratively performing the loop comprising blocks 1105-1119.During performance of the trip manager system 110 can dynamically updatea current route for the trip. In one example, manager system 110, duringperformance of a trip can dynamically update a current route for thetrip so that the current route for the trip dynamically transitionsbetween a single mode transportation route in which the current route isdesignated as a single transportation mode route into a multimodetransportation route in which the current trip is designated to beperformed during a first part in a user passenger vehicle transportationmode and in a second part is a user auxiliary vehicle modetransportation mode trip. For example, during an early iteration ofblock 1117, i.e., where a user passenger vehicle was proximate startinglocation A, manager system 110 can establish a current route for a tripas a single mode user passenger vehicle mode. However, at a lateriteration e.g. where a user passenger vehicle is at location A′ asdepicted in FIG. 6A, conditions may have changed, i.e. the predictedtraffic density through cells may increase, predicted weather in a cellmay change, or a user may execute an unplanned stop, thereby alteringthe predicted arrival time at a destination. Thus, in one scenariomanager system 110, during an iteration of generate block 1117, when auser passenger vehicle user is at location A′ manager system 110 canreturn an action decision to generate, as a current route for a trip, amultimode transportation route that comprises a first part performed inaccordance with a user passenger vehicle transportation mode and in asecond part a user auxiliary vehicle transportation mode in which theuser rides the user's auxiliary vehicle.

Reference is now made again to the flowchart of FIG. 4C. On thedetermination at block 1704 that there are N adjacent destination cellsin which a predicted performance of a user auxiliary vehicle through thecell exceeds a predicted performance of a user passenger vehicle throughthe cell manager system 110 can proceed to block 1705.

At block 1705 manager system 110 can establish a geofence that a cellborder closest to a current location of a user passenger vehicle of auser. For example, with reference to the geospatial map of FIG. 6A,according to one embodiment, a user passenger vehicle of the user can beat starting location A, using Eq. 1, the predicted performance of a userauxiliary vehicle through a cell can be predicted to exceed theperformance predicted performance of a user passenger vehicle throughthe cell for destination cells F2, E2, and D2. Thus, the cell borderclosest to a current location of the vehicle of a user passenger vehicleof the user in the described example can be the border between cell D2and cell C2. At block 1705 manager system 110 can establish geofence6014 at the border between cell D2 and cell C2 and at location andgeofence 6014 can be centered on location 6013 of the cell borderwherein location 6013 is the location at which the current routeintersects the border. Manager system 110 at block 1705 can establishgeofence 6014 to identify a breaking point between a user passengervehicle transportation mode and a user auxiliary vehicle transportationmode. At the breaking point, a user can exit the user's user passengervehicle and begin traveling using the user's user auxiliary vehicle.Based on Artificial Intelligence (AI) featurizations herein, thebreaking point can be a point identified to obtain a balance ofadvantages associated with both transportation in a user passengervehicle transportation mode and a user auxiliary vehicle transportationmode.

With geofence 6014 established at block 1705, manager system 110 atblock 1705 can identify open parking areas for a user passenger vehiclewithin geofence 6014. Predicted open parking spaces within geofence 6014can be determined based on identification of current open parking spaceswithin geofence 6014. For example, manager system 110 can examine mostrecently received IoT data received at block 1115, during the mostrecent iteration to identify currently open user passenger vehicleparking spots within geofence 6014. Manager system 110, with currentlyopen user passenger vehicle parking spots identified can querypredictive model 7004 to determine a likelihood of identified parkingspots remaining open at a time when the user passenger vehicle of theuser is predicted to reach the location of the open parking spot. Basedon the predicted likelihood exceeding a threshold manager system 110 canidentify a currently open user passenger vehicle parking spot as apredicted open parking spot predicted to remain open at a time when theuser passenger vehicle of the user reaches the identified parking spot.It will be seen with predicted open parking spots within geofence 6014identified manager system 110 can provide a plurality of alternativecandidate routes using each of the identified predicted open parkingspots. Manager system 110 at block 1706 can provide candidate routes foran auxiliary transportation mode using each of the identified predictedopen parking spaces identified at block 1705.

Referring to the geospatial map of FIG. 6B, manager system 110 canidentify passenger vehicle parking spaces 6004 as predicted open parkingspots associated to geofence 6014.

Manager system 110 can provide alternative candidate auxiliarytransportation mode routes for each of the predicted open parking spaceswithin geofence 6014. In FIG. 6B, representative candidate routes aredepicted for the predicted open parking spot at location B of FIG. 6B.Candidate routes for evaluation can be defined respectively by candidateroute segment 6042, candidate route segment 6043, and candidate routesegment 6044. Candidate route segment 6042 can include only roadwayroute segments between location B and destination location Z. Candidateroute segment 6043 can include route segments that comprise roadwayroute segments and can also include trail route segments, i.e.,comprising first and second trail segments 6022 as depicted proximatedestination location Z. According to one example manager system 110 canselect a candidate route having a route segment commencing at location Bas the selected route. In such an example, manager system 110 canestablish location B as the breaking point of a multimode transportationroute.

Manager system 100 can be configured so that one determining using Eq. 1that a user auxiliary vehicle mode is predicted to outperform a userpassenger vehicle mode, manager system 110 can evaluate candidatealternative routes that are further diversified to include publicvehicle transportation route segments. Candidate route segment 6044 caninclude roadway route segments and also a public transportation routesegment. That is, route segment 6032 can specify a route segment inwhich a transportation route comprises travel using publictransportation, e.g., a train. Accordingly, at generate block 1117manager system 110 in some cases can generate as a current selectedroute for a current trip a multimode route that includes a first one ormore route segment to be traveled in a user passenger vehicle mode, asecond one or more route segment to be traveled in a user auxiliarytransportation mode and a third one or more route segment to be traveledin a public vehicle transportation mode. Route segments 6042, 6043, and6044 respectively define candidate routes with the remaining portion ofthe route to be traveled between a current location (e.g. location A orA′) and the candidate break point being evaluated, e.g. location B.Manager system 110 for evaluating the various candidate routes havingvarious candidate breaking points can obtain optimum passenger vehicleroute data from a current location to the candidate breaking pointlocation from ground traffic service system 170.

Manager system 110 can evaluate the different candidate routes providedat block 1706 using Eq. 2 to score alternative candidate routes.According to one embodiment, scoring formula for scoring alternativeuser auxiliary vehicle routes can be provided as follows.P=RF ₁ W ₁ +RF ₂ W ₂ +RF ₃ W ₃ +RF ₄ W ₄  (Eq. 2)

Where P is a predicted performance score for a candidate route beingevaluated, where RF₁ is a predicted destination arrival time associatedto the candidate route, where RF₂ is a predicted comfort levelassociated to the route, where RF₃ is a predicted physical exhaustionfactor associated to the route, and where RF₄ is a current route factor.

Manager system 110 at block 1707 can score the various candidate routesusing Eq. 1 and at block 1708 can select the highest scoring candidateroute as the current selected route for a current trip. Manager system110 can assign a higher value under factor RF₁ where the predictedarrival time using the candidate route is earlier and can assign a lowervalue according to factor RF₁ where a predicted arrival time associatedto the route is lower. Manager system 110 for assigning values accordingto factor RF₁ can query predictive model 7002 which, as explained inconnection with FIG. 7A, may have been trained to predict average speedsassociated to different route segments in dependence on various queryfactors such as time classifiers, predicted weather condition factors,predicted travel density factors. For determining the predicted arrivaltime associated to a given candidate route segment, manager system 110can aggregate predicted intermediate location arrival times associatedto the various different route segments defining a candidate route.Manager system 110 applying factor RF₁ can also query predictive model7004 to adjust the predicted arrival time in dependence on predictedparking space availability.

Manager system 110 applying factor RF₂ can assign a higher value underfactor RF₂ where a user is predicted to be more comfortable duringperformance of the candidate route being evaluated and can assign alower value under factor RF₂ where the users predicted to be lesscomfortable during travel according to the candidate route.

Manager system 110 applying factor RF₃ can include manager system 110using the decision data structure of Table C to apply different comfortspecifying criterion sets in dependence on a user auxiliary vehiclebeing used by a user for a route segment or a set of route segmentsdefining a route. Where a route segment includes an associated enclosedenvironment as may be expected in the case that the user will betraveling in a public vehicle provided by a public train having a closeenvironment, the comfort level of a user can be expected to be lessimpacted by weather conditions. Accordingly, the criterion set of TableC associated to Row 3 can result in assigning according to factor RF₃ aweather condition in dependent on comfort level for the segment.Referring to the decision data structure of Table C, manager system 110applying criterion sets C001 or C002 can assign a comfort level independence on weather conditions, e.g. can assign relatively highcomfort level values according to factor RF₃ in the case of moderatelywarm temperatures with no precipitation and can assign relatively lowcomfort values applying criterion set C001 or C002 (Row 1 or Row 2) inthe case, e.g. that temperature is colder than a threshold value or inthe case a predicted weather condition for a route segment of thecandidate route specifies predicted weather conditions. It will be seenthat in the case that candidate routes are being evaluated, managersystem 110 can use current weather conditions or traffic conditions inthe case that a user is predicted to traverse the segment in the nearterm.

TABLE C Row Vehicle Type Criterion Set 1 User auxiliary vehicle -Motorized Scooter C001 2 User auxiliary vehicle - Bicycle C002 3 Publicvehicle - Train C003 . . . . . . . . .

Manager system 110 applying factor RF₃, which is a physical exhaustionfactor can assign higher values under factor RF₃ where a candidate routeis predicted to less significantly physically exhaust a user and canassign lower values under factor RF₃ in the case a candidate routesegment defining a route is predicted to more substantially physicallyexhaust a user. Manager system 110 applying factor RF₃ can includemanager system 110 using the decision data structure of Table D with useof the decision data structure of Table D, manager system 110 can selectthe criterion set for use in determining an assigned physical exhaustionvalue in dependence on vehicle type being used by a user for a routesegment.

TABLE D Row Vehicle Type Criterion Set 1 User auxiliary vehicle -Motorized Scooter D001 2 User auxiliary vehicle - Bicycle D002 3 Publicvehicle - Train D003 . . . . . . . . .

Referring to Table D, manually powered vehicles such as bicycles, asspecified in Row 2, can trigger action decisions to assign lowest valuesunder the physical exhaustion factor, factor RF₃ motorized userauxiliary vehicles, such as specified in Row 2 can trigger actiondecisions to assign a second lowest value under the physical exhaustionfactor and Row 3 (public vehicle) can trigger action decisions whereinhigher physical exhaustion scores are assigned under factor RF₄ with thehigher score indicating that physical exhaustion favorably isanticipated not to be significant and not to detract from performance ofa route segment.

Manager system 110 applying factor RF₅ can include manager system 110assigning a higher value according to factor RF₅, wherein the highervalue increases the likelihood of a current route being maintained andcan assign a lower value according to factor RF₅ in the case that ahigher value increases the likelihood of transitioning a current routeof a user to a different route. Embodiments herein recognize thatmaintaining an existing route can be favorable, e.g. in terms ofyielding improved understanding by a user of a route and less confusionto a user regarding a route being traversed by the user.

Manager system 110 at select route block 1708 can select the highestscoring route of the candidate routes, where candidate routes have beenprovided at block 1706. In the case that decision block 1704 yielded adetermination that the final destination cell has a higher predictedperformance for a user passenger vehicle transportation mode as comparedto a user auxiliary vehicle transportation mode, the route selection bymanager system 110 at block 1708 can comprise manager system 110generating by selecting the baseline route set at block 1702 as thecurrent route for a specified trip of a user. It will be seen, withreference to examine cells block 1703, that manager system 110 caniteratively return a decision to maintain a current user passengervehicle mode route until and unless an examination of one or moredestination cells yields a determination that an auxiliarytransportation mode route for the one or more destination cell ispredicted to outperform the user passenger vehicle transportation modefor the cells. With the current route selected, and thus generated atblock 1708, manager system 110 can proceed to block 1118 as depicted inFIG. 4B.

At block 1118, manager system 110 can provide one or more output independence on the route selected and generated at block 1117. At block1118, manager system 110 can send, e.g. prompting data and/or navigationdata for receipt by vehicle system 120A, user auxiliary vehicle system121A, and/or client computer device 130A respectively at blocks 1204,1214, and 1306. As set forth herein, the current route generated atblock 1117 by manager system 110 can include, e.g. a multimodetransportation route including a user passenger vehicle transportationroute and a user auxiliary vehicle transportation route, oralternatively can comprise a single mode transportation route, e.g.consisting entirely of a user passenger vehicle transportation route oralternatively entirely of a user auxiliary vehicle transportation route.In some embodiments, a user auxiliary vehicle transportation route canspecify that a user will transition vehicles, e.g. from a personalvehicle to a public transportation vehicle in the case of a userauxiliary vehicle transportation route.

Manager system 110 at block 1118 can send prompting data and/ornavigation data for implementation of the generated route generated atblock 1117. Prompting data can include data for presentment to a userthat prompts the user to take action so that the route is performed. Theprompting data can include, e.g. audio prompting data and/or visualprompting data that can be presented in prompting area 5016 of userinterface 5000 as set forth in FIG. 5 for display on display of a clientcomputer device or user interface 5000 can additionally or alternativelybe displayed on a display for viewing by a user on a user interfacedisplay of vehicle system 120A or user auxiliary vehicle system 121A. Inthe case manager system 110 at block 1118 sends navigation data, thereceiving system or device, i.e. vehicle system 120A, user auxiliaryvehicle system 121A, and/or client computer device 130A canautomatically perform navigation in accordance with the navigation dataso that the user is automatically transported along the current routetoward a destination without manual action by the user. For example, insuch a described embodiment, vehicle system 120A can define aself-driving user passenger vehicle and user auxiliary vehicle 121A candefine a self-driving user auxiliary vehicle. Manager system 110 oncompletion of block 1118 can proceed to block 1119 to determine whetherthe user is within a parking geofence.

Referring again to the geospatial map of FIG. 6B, manager system 110 canselect as a current route for a current trip a multimode transportationroute in which a user can travel from start location A to intermediatelocation B as shown in FIG. 6B, and at location B the user cantransition into a user auxiliary vehicle transportation mode, e.g. toride a motorized scooter of the user or another alternative vehicleuntil arriving at destination Z. In the case that manger system 110generated a multimode route as the current route, prompting data sent atblock 1118 which can be presented in prompting area 5016 of userinterface 5000 of FIG. 5 can include prompting data to prompt the user,e.g. to travel at the predicted open parking spot associated to locationB and can further include prompting data to prompt the user whenarriving at the predicted open parking space associated to location B todisembark the user passenger vehicle of the user and transition intotravelling in a user auxiliary vehicle transportation mode, e.g. ridinga motorized scooter of the user (or an alternative user auxiliaryvehicle commencing at location B).

According to one embodiment, when a user is within a geofence 6064defined about a destination location, e.g. the location defined bydestination Z as specified in FIG. 6A, manager system 110 can transitioninto a parking guiding mode of operation that is described withreference to blocks 1120-1124.

In the case that a user has not breached a parking geofence, managersystem 110 can return to block 1104. In the iterative performance of1105-1119 manager system 110 can be iteratively performing a sequence ofblocks including generate block 1117, which can under certain scenarios,result in a certain route being updated.

Referring again to FIG. 6B, manager system 110 for providing a pluralityof candidate routes can provide a plurality of candidate routesencompassing the predicted open parking space at location B as well asall passenger vehicle parking spaces 6004 predicted to be open parkingspaces within geofence 6014. According to one example, using Eq. 2,manager system 110 at block 1707, can select candidate route 6043 asdepicted in FIG. 6B as the selected segment defining (with an additionalone or more route segment preceding location B to be traversed in a userpassenger vehicle transportation mode) the currently generated selectedroute.

Referring to the loop of blocks 1105-1119, manager system 110 caniteratively perform generate block 1117 and can iteratively, i.e. whencertain conditions apply, updating the current route of a user. Forexample, FIG. 6C depicts the case where a user has transitioned betweenlocation B and location C. At a succession of points between location Band C, manager system 110 can have iteratively reperformed generateblock 1117 and, according to one example, may have maintained thecurrent route during each iteration. When a current route is re-selectedall route segment defining the route from a current location to thedestination location were included in the previous selected route.Referring to location C as depicted in FIG. 6C, it will be seen that auser has now successfully transitioned at breaking point B from a userpassenger vehicle transportation mode to a user auxiliary vehicletransportation mode and may be travelling by riding on a personaltransportation vehicle of the user, e.g. a motorized scooter or amanually driven bicycle.

Referring again to the flowchart of FIG. 4C, manager system 110 at block1701 with the user now at location C can determine that a user auxiliaryvehicle system of a user is not traveling with a vehicle system of auser (which by examined of location data from vehicle system 120A can bedetermined to be parked at B) and accordingly can proceed to block 1709.At block 1709, manager system 110 can determine whether a user auxiliaryvehicle system is traveling with a user while a vehicle system of theuser has stopped, e.g. has parked. In the case that manager system 110determines that a user auxiliary vehicle system of a user is moving withthe vehicle system associated to the user having been stopped(indicating that the user passenger vehicle user has parked at locationB) manager system 110 can proceed to block 1710 (auxiliarytransportation mode). In an auxiliary transportation mode, a user can betraveling in a user auxiliary vehicle transportation mode or a publicvehicle transportation mode.

Referring to blocks 1701 and 1709 manager system 110 can determine acurrent transportation mode of a user. In the case manager system 110determines that a user auxiliary vehicle is traveling with a userpassenger vehicle (block 1701) thus determining that the user istraveling in a user passenger vehicle transportation mode, evaluation ofa current route by manager system 110 can include performing of cellprocessing as explained in connection with Eq. 1 and FIG. 6B and canalso include evaluating of candidate routes having multiple candidatebreak points at which the user is to transition from a user passengervehicle transportation mode to a user auxiliary vehicle transportationmode, and selecting one of the candidate routes. In the case onexamination path data manager system 110 determines that a userauxiliary vehicle is traveling with a user passenger vehicle stopped(block 1709) thus determining that the user is traveling in a userauxiliary vehicle transportation mode, evaluation of a current route bymanager system 110 can include evaluating of candidate routes commencingat the current location of the user, and selecting one of the candidateroutes.

Referring to blocks 1701 and 1709 manager system 110 can determine acurrent transportation mode of a user using path data of a userpassenger vehicle and path data of a user auxiliary vehicle as set forthherein. Referring to blocks 1701 and 1709 manager system 110additionally or alternatively can determine a current transportationmode of a using proximity data that indicates that user passengervehicle 1200 and user auxiliary vehicle 1210 and/or a client computerdevice e.g. client computer device 130A are in proximity with oneanother. For example, each of vehicle system 120A and auxiliary vehiclesystem 121A and client computer device 130A (FIG. 3 ) can haveincorporated therein a short range radio transceiver such that thetransceivers communicate in short range and do not incorporate when notin short range. One or more of vehicle system 120A and auxiliary vehiclesystem 121A or client computer device 130A can send at blocks 1203,1213, or 1305 respectively the proximity indicating communicationactive/inactive status data to manager system 110. Manager system 110 inturn can determine that a user is traveling in a user passenger vehiclesystem when vehicle system 120A, auxiliary vehicle system 121A, andclient computer device 130A are in short range radio transceivercommunication range, and can determine that a user is traveling in auser auxiliary vehicle transportation mode when auxiliary vehicle system121A, and client computer device 130A are in short range radiotransceiver communication range, but neither of auxiliary vehicle system121A, and client computer device 130A are in short range radiotransceiver communication range of vehicle system 120A.

Manager system 110 can determine that a user is traveling in a publicvehicle transportation mode when a user is completing a current triphaving a current route specifying a public vehicle transportation moderoute segment, client computer device 130A is not in short range radiotransceiver communication range of vehicle system 120A but nonethelessbased on examination of path data is travelling in excess of a thresholdspeed indicative of a maximum speed of the user's auxiliary vehicle. Themethod depicted with the flowchart of FIG. 4C according to oneembodiment can have a third branch (not shown) triggered when managersystem 110 determines that a user traveling in a public vehicletransportation mode. Triggering of the branch can trigger functionalblocks according to functional blocks 1710, 1711, 1712.

Referring again to block 1710, manager system 110 can provide candidateroutes of a user based on a current location of a user, i.e. thelocation of a user at location C. Exemplary candidate routes of a userstarting from location C are depicted in FIG. 6C. The candidate routesof a user starting from location C and terminating at destinationlocation Z can include candidate route 6052, candidate route 6053, andcandidate route 6054. At block 1710, manager system 110 can provide aplurality of candidate routes and at block 1711, manager system 110 canscore the various candidate routes. The scoring at blocks 1710 caninclude manager system 110 using Eq. 2 as set forth herein for scoringthe various candidate routes in dependence, e.g. on a destination timefactor RF₁, a comfort factor RF₂, a physical exhaustion factor RF₃, anda current route factor RF₄. With no changes in predicted current trafficconditions or weather conditions, it can be expected that manager system110, when a user has reached the interim location C, can re-select thecurrent route (where all route segments are shared by the previousselected route). However, in some circumstances and some scenarios,conditions may have changed.

For example, while the user has transitioned from location B to locationC, weather conditions or predicted weather conditions might havechanged. Also, as the user has transitioned from location B to locationC, traffic density conditions and/or predicted traffic densityconditions may have changed. In can be seen that by application of Eq. 2the highlighted change conditions or other change conditions canconceivably change scoring of candidate routes performed by applicationof Eq. 2, thus changing the selected route segment and thus triggering achange in the selected route so that a new current route is defined. Forexample, with reference to FIG. 6C, a change in weather conditions tocurrent storm conditions or predicted storm conditions can trigger theassignment of a low comfort value for a user, according to factor RF₂ ofEq. 2 to result in selection of candidate route 6054 depicted in FIG. 6Cover candidate route 6053. It can be seen that candidate route 6054which comprises the user traveling on an auxiliary transportationvehicle provided by a public train provides protection from theenvironment to the user and therefore can be more comfortable to a userwhen weather conditions change for the worse. In an alternativescenario, the user prior to reach interim location C, might havestopped, e.g. for any arbitrary reason, to change the timing parametersof the current route. Such changed timing parameters might have madecandidate route 6054 more attractive based by application of the scoringformula of Eq. 2, based e.g. on reduced wait time at a train terminal(with new timing conditions a terminal wait time might have be reducedthus boosting the factor RF₁ value for the route comprising a publicvehicle transportation mode).

Referring to the decision data structures of Tables A-D, it has beendescribed that manager system 110 can iteratively update the criterionsets associated with each action decision row of the various decisiontables. In another aspect, manager system 110 can iteratively updatecriterion sets in accordance with biometric data, which is iterativelyobtained from a user during an iterative performance by manager system110 of block 1112, wherein manager system 110 can receive location datafrom a client computer device 130A associated to a user, including suchdata as location data and also such data as biometric data of a user.For example, the client computer device of a user e.g. client computerdevice 130A can in one example be provided by a wrist worn smartwatchwhich includes one or more biometric sensor such as a pulmonary sensor,which senses, e.g. heart rate and/or blood pressure and which outputs tomanager system 110 biometric data in the form of e.g. heart rate dataand/or blood pressure data. Manager system 110 on receipt of biometricdata at block 1112 can automatically process the biometric data toidentify a risk condition associated to the user, e.g. a high bloodpressure condition of the user or a high heart rate, e.g. thresholdexceeding blood pressure or heart rate condition of a user. Based on thedetection of a risk condition by manager system 110, manager system 110can update a decision data structure such as the decision data structureof Tables A-D so that on the detection of a current risk condition, forexample, the weather conditions triggering a relatively high comfortvalue for a user under factor RF₂ of Eq. 2 or factor F₃ of Eq. 1 can berendered more stringent, i.e. more difficult to achieve. For example,prior to a risk condition of a user being detected a predicted weathercondition temperature returning a certain high comfort value for a useraccording to factor of Eq. 1 or Eq. 2, e.g. can change from atemperature range of 60° F. to 90° F. (no risk condition detection) to asmaller range (70° F. to 80° F.) in the case that a risk condition ofthe user is detected. According to one embodiment, in the case that arisk condition of a user is currently detected, manager system 110 canpredict for purposes of safety that the risk condition will bemaintained throughout the performance of a route from a current locationof a user to a destination location of a user. Manager system 110accordingly on detecting that a risk condition attaches to a user basedon examining of biometric data, can transition a current route of theuser to an updated route which includes one or more route segment inwhich the user travels in a public vehicle transportation mode. Forexample, in the described scenario the temperature can be 85° F. whichnormally drives a high comfort score for the user but does not drive ahigh comfort score for the user when a risk condition of the user isdetected can result in selection of a candidate route comprising one ormore route segment to be traveled in a public vehicle transportationmode.

According to another example, manager system 110 can be configured sothat detection of a risk condition of a user triggers a biasing ofcriterion set values as described in Table B and Table C so that thephysical exhaustion score under factor F4 Eq. 1 and RF3 of Eq. 2 isdriven to a second lowest possible value (second high predicted physicalexhaustion level) in the case the candidate route has the user riding onan a motorized user auxiliary vehicle, and to a lowest possible value(highest predicted physical exhaustion level) in the case the candidateroute has the user riding on an unmotorized user auxiliary vehicle.Under the alternative scenario described, a detection of a riskcondition of a user can drive transition of a selected route to a routethat includes a public vehicle transportation mode. For safety reasons,manager system 110 on detection of a risk condition of the user canperform evaluations of candidate routes and cells based on theassumption that the risk condition will remain for the duration of acurrent trip.

Returning to the flowchart of FIGS. 4A-4B, manager system 110 at block1119 can determine that a user has breached a parking geofence 6064associated to a destination location Z of the user. Manager system 110at block 1119 can determine that a user has breached parking geofence bydetermining that, e.g. client computer device 130A has breached theparking geofence, that user auxiliary vehicle system 121A has breachedthe geofence (in the case of a user auxiliary vehicle transportationmode), or can determine that vehicle system 120A with user auxiliaryvehicle system 121A has breached the parking geofence 6104, e.g. in thecase of a user passenger vehicle transportation mode. In response to thedetermination that a parking geofence has been breached at block 1119,manager system 110 can proceed to a guided parking mode of operation asdepicted in blocks 1120-1124.

At block 1120 for example, manager system 110 can query an IoT sensorsystem 140 which receives the query at block 1403 and sends return data,i.e. as may be provided by IoT data at block 1404. Manager system 110can receive the response IoT data at block 1121 and can performprocessing of the received IoT data at block 1122, the IoT data receivedat block 1121 can include, e.g. spatial image data from IoT camerasensors disposed to sense occupancy or openness of parking spaces withinthe parking geofence established about destination location Z. The framerate can be increased with respect to the frame rate associated to IoTdata received at block 1115 and the higher frame rate IoT data can beselectively generated only within parking geofence 6064 generated aboutdestination location Z.

At block 1123, manager system 110 can send prompting data to, e.g.vehicle system 120A, user auxiliary vehicle system 121A, and clientcomputer device 130A for receipt respectively by vehicle system 120A,user auxiliary vehicle system 121A, and client computer device 130A atrespective blocks 1205, 1215, and 1307. Manager system 110 can send, atblock 1123, prompting data that prompts a user to travel to a detectedopen parking space within the geofence detected by performance of theprocessing at block 1122.

At block 1124, manager system 110 can determine whether the user hasstopped at the destination location Z. Manager system 110 can determineat block 1124 that the user has stopped at the destination location Z,when the user has parked the user's user passenger vehicle and/or userauxiliary vehicle in a detected open parking spot detected at block1122. In the case that a user has been determined not to be stopped atblock 1124, manager system 110 can return to block 1119 and caniteratively return the loop of blocks 1119-1124 until a user has parkeda user passenger vehicle of the user and/or a user auxiliary vehicle ofthe user at an identified open parking space identified at block 1122.According to one embodiment, with the receipt of IoT data received atblock 1121, manager system 110 can be iteratively receiving from vehiclesystem 120A, user auxiliary vehicle system 121A, and client computerdevice 130A updated location data specifying current location of vehiclesystem 120A, user auxiliary vehicle system 121A, and client computerdevice 130A. Manager system 110 can determine that a user has stopped atan open parking space at block 1124 by examining one or more of vehiclesystem location data, current user auxiliary vehicle system locationdata, and/or current client computer device location data. All of whichcan be received iteratively at block 1121. Manager system 110 oncompletion of block 1124 can proceed to block 1125 to perform machinelearning training.

At block 1125, manager system 110 can apply training data for trainingof predictive models 7002 and 7004 as described in connection with FIGS.7A and 7B, based on historical route data, historical weather conditiondata, and historic traffic condition data associated to a trip and routejust completed at block 1124. Such machine learning training performedby manager system 110 can include multiple queries of data repository108 which can store the predictive models 7002 and 7004 and predictivemodels 7002 and 7004 once retrained with updated training data can beupdated within data repository 108 as indicated by query receive respondand update block 1087 performed by data repository 108. On completion ofblock 1125, manager system 110 can proceed to block 1126 at whichmanager system 110 can return to block 1104 where manager system 110 canwait for route request data. Manager system 110 can be executingmultiple instances of block 1104 and succeeding blocks concurrently.

At block 1104, manager system 110 can wait for receipt of additionalroute request data from the user of client computer device 130A-130Z.Manager system 110 can be iteratively performing multiple instances ofthe loop of blocks 1104-1105 for multiple users of system 100 who haveassociated respective different client computer devices 130A-130Z whomay have defined and submitted route request data concurrently.

Various available tools, libraries, and/or services can be utilized forimplementation of predictive model 7002 and predictive model 7004. Forexample, a machine learning service can provide access to libraries andexecutable code for support of machine learning functions. A machinelearning service can provide access set of REST APIs that can be calledfrom any programming language and that permit the integration ofpredictive analytics into any application. Enabled REST APIs can providee.g. retrieval of metadata for a given predictive model, deployment ofmodels and management of deployed models, online deployment, scoring,batch deployment, stream deployment, monitoring and retraining deployedmodels. According to one possible implementation, a machine learningservice provided by IBM® WATSON® can provide access to libraries ofAPACHE® SPARK® and IBM® SPSS® (IBM® WATSON® and SPSS® are registeredtrademarks of International Business Machines Corporation and APACHE®and SPARK® are registered trademarks of the Apache Software Foundation.A machine learning service provided by IBM® WATSON® can provide accessset of REST APIs that can be called from any programming language andthat permit the integration of predictive analytics into anyapplication. Enabled REST APIs can provide e.g. retrieval of metadatafor a given predictive model, deployment of models and management ofdeployed models, online deployment, scoring, batch deployment, streamdeployment, monitoring and retraining deployed models. Trainingpredictive model 7002 and predictive model 7004 can include use of e.g.support vector machines (SVM), Bayesian networks, neural networks and/orother machine learning technologies.

Certain embodiments herein may offer various technical computingadvantages involving computing advantages to address problems arisingparticularly in the realm of computer networks and computer systemsconfigured to provide location based services (LBS). Embodiments hereincan employ predictive analytics which use Artificial Intelligence (AI)platforms. Embodiments herein can evaluate predicted performance of auser traveling in a user passenger vehicle transportation mode through ageospatial region as compared to predicted performance of a usertraveling through a geospatial region in a user auxiliary vehicletransportation mode, in which the user can be traveling on a personalvehicle of the user. A computer system can be employed to perform thedescribed evaluating on an iterative basis so that returned actiondecision can change in dependence on changing sense conditions. Senseconditions that can trigger changes can include e.g. weather conditionchains and/or traffic condition changes. Embodiments herein can includea computer system transitioning can trigger the transition of a currentroute to be designated perform performance by a user traveling in asingle mode transportation route into multimode mode transportation modein which the user travels on a trip from a starting location to adestination location partially in a user passenger vehicletransportation mode and partially in a user auxiliary vehicletransportation mode. Embodiments herein can facilitate a user arrivingat a destination e.g. more quickly and with greater comfort. Embodimentsherein can base route planning on a predicted comfort level of a user.The user's predicted comfort level can dynamically change throughout thecourse of the trip in dependence, e.g., on changing weather conditionsand/or changing biometric conditions of the user. Embodiments herein cansense biometric risk condition of the user and can dynamically changecriterion for determination of the sense of a predicted comfort level ofthe user. Embodiments herein can include use of predictive models thatare trained by machine learning to predict e.g. a average speed of auser passenger vehicle or user auxiliary vehicle through a geospatialregion or along a route segment. Embodiments herein can includedynamically adding to a data repository area that stores data on trailsegments that are determined to be traversable by user traveling on auser auxiliary vehicle such as a personal vehicle. Embodiments hereincan perform route planning in dependence on predicted physicalexhaustion levels of a user during performance of the route and decisiondata structures can be employed e.g. for return of action decisions inrespect to a predicted users physical exhaustion level, which predictedlevels can be dependent on vehicle types used for travel by the user.

FIGS. 8-10 depict various aspects of computing, including a computersystem and cloud computing, in accordance with one or more aspects setforth herein.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 8 , a schematic of an example of a computing nodeis shown. Computing node 10 is only one example of a computing nodesuitable for use as a cloud computing node and is not intended tosuggest any limitation as to the scope of use or functionality ofembodiments of the invention described herein. Regardless, computingnode 10 is capable of being implemented and/or performing any of thefunctionality set forth hereinabove. Computing node 10 can beimplemented as a cloud computing node in a cloud computing environment,or can be implemented as a computing node in a computing environmentother than a cloud computing environment.

In computing node 10 there is a computer system 12, which is operationalwith numerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with computer system 12 include, but are not limited to, personalcomputer systems, server computer systems, thin clients, thick clients,hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system 12 may be described in the general context of computersystem-executable instructions, such as program processes, beingexecuted by a computer system. Generally, program processes may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program processes may belocated in both local and remote computer system storage media includingmemory storage devices.

As shown in FIG. 8 , computer system 12 in computing node 10 is shown inthe form of a computing device. The components of computer system 12 mayinclude, but are not limited to, one or more processor 16, a systemmemory 28, and a bus 18 that couples various system components includingsystem memory 28 to processor 16. In one embodiment, computing node 10is a computing node of a non-cloud computing environment. In oneembodiment, computing node 10 is a computing node of a cloud computingenvironment as set forth herein in connection with FIGS. 9-10 .

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system 12 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system 12, and it includes both volatile and non-volatilemedia, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program processes that are configured to carry out thefunctions of embodiments of the invention.

One or more program 40, having a set (at least one) of program processes42, may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram processes, and program data. One or more program 40 includingprogram processes 42 can generally carry out the functions set forthherein. In one embodiment, manager system 110 can include one or morecomputing node 10 and can include one or more program 40 for performingfunctions described with reference to method 200 of FIG. 2 and functionsdescribed with reference to manager system 110 as set forth in theflowchart of FIGS. 4A and 4B, and the flowchart of FIG. 4C. In oneembodiment, vehicle system 120A can include one or more computing node10 and can include one or more program 40 for performing functionsdescribed with reference to vehicle system 120A as set forth in theflowchart of FIGS. 4A and 4B. In one embodiment, auxiliary vehiclesystem 121A can include one or more computing node 10 and can includeone or more program 40 for performing functions described with referenceto vehicle system 121A as set forth in the flowchart of FIGS. 4A and 4B.In one embodiment, client computer device 130A can include one or morecomputing node 10 and can include one or more program 40 for performingfunctions described with reference to client computer device 130A as setforth in the flowchart of FIGS. 4A and 4B. In one embodiment, systems140, 150, 160, 170, 172, 180 can include one or more computing node 10and can include one or more program 40 for performing functionsdescribed with reference to systems 140, 150, 160, 170, 172, 180 as setforth in the flowchart of FIGS. 4A and 4B. In one embodiment, thecomputing node based systems and devices depicted in FIG. 1 can includeone or more program for performing function described with reference tosuch computing node based systems and devices.

Computer system 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computer system12; and/or any devices (e.g., network card, modem, etc.) that enablecomputer system 12 to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O) interfaces22. Still yet, computer system 12 can communicate with one or morenetworks such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter20. As depicted, network adapter 20 communicates with the othercomponents of computer system 12 via bus 18. It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with computer system 12. Examples, include, butare not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, and dataarchival storage systems, etc. In addition to or in place of havingexternal devices 14 and display 24, which can be configured to provideuser interface functionality, computing node 10 in one embodiment caninclude display 25 connected to bus 18. In one embodiment, display 25can be configured as a touch screen display and can be configured toprovide user interface functionality, e.g. can facilitate virtualkeyboard functionality and input of total data. Computer system 12 inone embodiment can also include one or more sensor device 27 connectedto bus 18. One or more sensor device 27 can alternatively be connectedthrough I/O interface(s) 22. One or more sensor device 27 can include aGlobal Positioning Sensor (GPS) device in one embodiment and can beconfigured to provide a location of computing node 10. In oneembodiment, one or more sensor device 27 can alternatively or inaddition include, e.g., one or more of a camera, a gyroscope, atemperature sensor, a humidity sensor, a pulse sensor, a blood pressure(bp) sensor or an audio input device. Computer system 12 can include oneor more network adapter 20. In FIG. 9 computing node 10 is described asbeing implemented in a cloud computing environment and accordingly isreferred to as a cloud computing node in the context of FIG. 9 .

Referring now to FIG. 9 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 9 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 10 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 9 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 10 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and processing components 96 for generatingroutes as set forth herein. The processing components 96 can beimplemented with use of one or more program 40 described in FIG. 8 .

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprise” (and any form ofcomprise, such as “comprises” and “comprising”), “have” (and any form ofhave, such as “has” and “having”), “include” (and any form of include,such as “includes” and “including”), and “contain” (and any form ofcontain, such as “contains” and “containing”) are open-ended linkingverbs. As a result, a method or device that “comprises,” “has,”“includes,” or “contains” one or more steps or elements possesses thoseone or more steps or elements, but is not limited to possessing onlythose one or more steps or elements. Likewise, a step of a method or anelement of a device that “comprises,” “has,” “includes,” or “contains”one or more features possesses those one or more features, but is notlimited to possessing only those one or more features. Forms of the term“based on” herein encompass relationships where an element is partiallybased on as well as relationships where an element is entirely based on.Methods, products and systems described as having a certain number ofelements can be practiced with less than or greater than the certainnumber of elements. Furthermore, a device or structure that isconfigured in a certain way is configured in at least that way, but mayalso be configured in ways that are not listed.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below, if any, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description set forth herein has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of one or more aspects set forth herein and the practicalapplication, and to enable others of ordinary skill in the art tounderstand one or more aspects as described herein for variousembodiments with various modifications as are suited to the particularuse contemplated.

The invention claimed is:
 1. A computer implemented method comprising:determining a current transportation mode of a user with use of data ofa user passenger vehicle associated to the user and data of a userauxiliary passenger vehicle associated to the user, wherein the userpassenger vehicle is capable of carrying the user auxiliary vehicle, andwherein the user auxiliary vehicle is configured to be hand carried bythe user; evaluating a current route of the user in dependence on thecurrent transportation mode of the user as determined by thedetermining; and providing one or more output in dependence on theevaluating, wherein the method includes iteratively performing thedetermining and the evaluating, wherein: the evaluating during a firstearlier instance of the evaluating includes updating the current routeof the user as a single mode user passenger vehicle transportationroute, and wherein the evaluating during a second later instance of theevaluating includes updating of the current route of the user as amultimode transportation route in which the user travels partially in auser passenger vehicle transportation mode and partially in a userauxiliary vehicle transportation mode, wherein the iteration includes(i) evaluating candidate routes commencing from a current location ofthe user, and (ii) selecting one of the candidate routes from (i),wherein the evaluating of candidate routes (i) includes applying acriteria having one or more factor selected from the group consisting ofRF₁, RF₂, RF₃, and RF₄, where RF₁ is a predicted destination arrivaltime associated with each respective candidate route of the candidateroutes, where RF₂ is a predicted comfort level associated with eachrespective route of the candidate routes, in dependence on a predictedcomfort level of the user when travelling according to the respectivecandidate route, where RF₃ is a predicted physical exhaustion factorassociated to the route in dependence on a predicted physical exhaustionlevel of the user when travelling according to the candidate route, andwhere RF₄ is a current route factor which biases the selection ofcandidate routes in favor of re-selecting a current route.
 2. A computerimplemented method comprising: determining a current transportation modeof a user with use of data of a user passenger vehicle associated to theuser and data of a user auxiliary passenger vehicle associated to theuser, wherein the user passenger vehicle is capable of carrying the userauxiliary vehicle, and wherein the user auxiliary vehicle is configuredto be hand carried by the user; evaluating a current route of the userin dependence on the current transportation mode of the user asdetermined by the determining; and providing one or more output independence on the evaluating, wherein the determining a currenttransportation mode of a user includes determining that the user iscurrently traveling in a user passenger vehicle transportation mode,wherein the evaluating a current route includes evaluating a predictedperformance of the user passenger vehicle through a geospatial regionassociated to a destination of the user in comparison to a predictedperformance of the user auxiliary vehicle through the geospatial regionassociated to the destination of the user, and wherein the methodincludes for predicting performance of the user passenger vehicle andthe user auxiliary vehicle through the geospatial region include using acriteria for ascertaining predicted performance of the user passengervehicle and a user auxiliary vehicle, respectively, through thegeospatial region, wherein the criteria includes one or more factorselected from the group consisting of F₁, F₂, F₃, F₄, and F₅, wherein F₁is an average speed factor in dependence on predicted average vehiclespeed through the geospatial region, F₂ is a parking factor independence on predicted availability of vehicle parking within thegeospatial region, F₃ is a comfort factor in dependence on predicteduser comfort through the geospatial region, F₄ is a physical exhaustionfactor in dependence on predicted level of physical exhaustion of theuser through the geospatial region, and F₅ is a current route factorwhich biases the selection of candidate routes in favor of re-selectinga current route.
 3. The computer implemented method of claim 2, whereinthe method includes determining that a predicted performance of the userauxiliary vehicle through the geospatial region exceeds the predictedperformance of the user passenger vehicle through the geospatial regionand based on the determining, generating a multimode transportationroute in which the user will travel to the destination partially in auser passenger vehicle transportation mode and partially in a userauxiliary vehicle transportation mode.
 4. The computer implementedmethod of claim 2, wherein the method includes iteratively performingthe determining and the evaluating, wherein the determining during afirst earlier iteration includes determining that the user passengervehicle of the user and the auxiliary vehicle are traveling together,and wherein the evaluating during the first earlier iteration includes(a) performing geospatial region processing to compare predictedperformance of the user passenger vehicle to predicted performance ofthe use auxiliary vehicle through a geospatial region encompassing adestination of a current trip of a user, (b) evaluating candidate routeshaving multiple different candidate break points at which the user is totransition from a user passenger vehicle transportation mode to a userauxiliary vehicle transportation mode, and (c) selecting one of thecandidate routes from (b), wherein the evaluating during a second lateriteration includes (i) evaluating candidate routes commencing from acurrent location of the user, and (ii) selecting one of the candidateroutes from (i).
 5. The computer implemented method of claim 2, whereinthe method for predicting performance of the user passenger vehicle andthe user auxiliary vehicle through the geospatial region includes usingthe formula S=F₁W₁+F₂W₂+F₃W₃+F₄W₄+F₅W₅, for scoring predictedperformance of the user passenger vehicle and a user auxiliary vehicle,respectively, through the geospatial region, and where W₁-W₅ are weightsassociated to the various factors.
 6. The computer implemented method ofclaim 2, wherein the method includes iteratively performing thedetermining and the evaluating, wherein the determining during a firstearlier iteration includes determining that the user passenger vehicleof the user and the auxiliary vehicle are traveling together, andwherein the evaluating during the first earlier iteration includes (a)performing geospatial region processing to compare predicted performanceof the user passenger vehicle to predicted performance of the useauxiliary vehicle through a geospatial region encompassing a destinationof a current trip of a user, (b) evaluating candidate routes havingmultiple different candidate break points at which the user is totransition from a user passenger vehicle transportation mode to a userauxiliary vehicle transportation mode and (c) selecting one of thecandidate routes from (b), wherein the evaluating during a second lateriteration includes (i) evaluating candidate routes commencing from acurrent location of the user, and (ii) selecting one of the candidateroutes from (i), wherein the evaluating of candidate routes of (i)includes using the formula P=RF₁W₁+RF₂W₂+RF₃W₃+RF₄W₄, where P is apredicted performance score for a candidate route being evaluated, whereRF₁ is a predicted destination arrival time associated to the candidateroute, where RF₂ is a predicted comfort level associated to the route independence on a predicted comfort level of the user when travellingaccording to the candidate route, where RF₃ is a predicted physicalexhaustion factor associated to the route in dependence on a predictedphysical exhaustion level of the user when travelling according to thecandidate route, and where RF₄ is a current route factor which biasesthe selection of candidate routes in favor of re-selecting a currentroute.
 7. The computer implemented method of claim 2, wherein the methodincludes receiving route data from the user that specifies a currenttrip having a destination, and wherein the current route is for thecurrent trip, wherein the method includes iteratively performing thedetermining and the evaluating to iteratively update the current routefor the current trip, wherein the method includes dynamically updatingthe current route for the current trip from a first route to a secondroute, wherein the first route is a single transportation mode route inwhich the user is designated to travel to the destination in a userpassenger vehicle transportation mode, wherein the second route is amultimode transporting route in which the user is designated to travelto the destination partially in a user passenger vehicle transportationmode and partially in a user auxiliary vehicle transportation mode. 8.The computer implemented method of claim 2, wherein the method includesreceiving route data from the user that specifies a current trip havinga destination, wherein the current route is for the current trip,wherein the method includes iteratively performing the determining andthe evaluating to iteratively update the current route for the currenttrip, wherein the method includes dynamically updating the current routefor the current trip from a first route to a second route, wherein thefirst route is a single transportation mode route in which the user isdesignated to travel to the destination in a user passenger vehicletransportation mode, wherein the second route is a multimodetransporting route in which the user is designated to travel to thedestination partially in a user passenger vehicle transportation modeand partially in a user auxiliary vehicle transportation mode, whereinthe method includes in response to the dynamically updating the currentroute to the first route, sending to the user prompting data, theprompting data including text based data describing to the user actionsby the user for performance of the first route, wherein the methodincludes in response to the dynamically updating the current route fromthe first route to the second route, sending second prompting data tothe user, the second prompting data including text based data describingto the user actions by the user for performance of the second route. 9.The computer implemented method of claim 2, wherein the method includesreceiving route data from the user that specifies a current trip havinga destination, wherein the current route is for the current trip,wherein the method includes iteratively performing the determining andthe evaluating to iteratively update the current route for the currenttrip, wherein the method includes dynamically updating the current routefor the current trip from a first route to a second route, wherein thefirst route is a single transportation mode route in which the user isdesignated to travel to the destination in a user passenger vehicletransportation mode, wherein the second route is a multimodetransporting route in which the user is designated to travel to thedestination partially in a user passenger vehicle transportation modeand partially in a user auxiliary vehicle transportation mode, whereinthe method includes in response to dynamically updating the currentroute to the first route, sending to the user prompting data, theprompting data including text based data describing to the user actionsby the user for performance of the first route, wherein the methodincludes in response to the dynamically updating the current route fromthe first route to the second route, sending second prompting data tothe user, the second prompting data including text based data describingto the user actions by the user for performance of the second route,wherein the text based data describing to the user actions by the userfor performance of the second route includes text base data promptingthe user to disembark the user passenger vehicle at a specifiedlocation, and to commence travel by riding on the user auxiliary vehicleat the specified location.
 10. The computer implemented method of claim2, wherein the method includes iteratively performing the determiningand the evaluating, and wherein method includes updating, withperformance of an iteration of the evaluating, a current route from afirst route which includes user auxiliary vehicle transportation modeand is absent of a second transportation mode to a second route whichincludes the second transportation mode, in response to examination ofbiometric data received from a device worn by the user.
 11. The computerimplemented method of claim 2, wherein the method includes iterativelyperforming the determining and the evaluating, wherein the determiningduring at least one iteration includes (i) evaluating candidate routescommencing from a current location of the user, and (ii) selecting oneof the candidate routes from (i), wherein the evaluating of candidateroutes of (i) includes applying a criteria having one or more factorsselected from the group consisting of RF₁, RF₂, RF₃, and RF₄, where RF₁is a predicted destination arrival time associated to the candidateroute, where RF₂ is a predicted comfort level associated to the route independence on a predicted comfort level of the user when travellingaccording to the candidate route, where RF₃ is a predicted physicalexhaustion factor associated to the route in dependence on a predictedphysical exhaustion level of the user when travelling according to thecandidate route, and where RF₄ is a current route factor which biasesthe selection of candidate routes in favor of re-selecting a currentroute.
 12. The computer implemented method of claim 2, wherein themethod includes iteratively performing the determining and theevaluating.
 13. The computer implemented method of claim 2, wherein themethod includes iteratively performing the determining and theevaluating, and wherein method includes iteratively updating the currentroute of the user in dependence on the iteratively performing thedetermining and the evaluating.
 14. The computer implemented method ofclaim 2, wherein the method includes iteratively performing thedetermining and the evaluating, wherein the iteratively evaluatingincludes, for respective instances of the evaluating, comparingpredicted performances of multiple candidate routes including thecurrent route, and wherein the method further includes iterativelyupdating the current route of the user based on the predictedperformance evaluation.
 15. The computer implemented method of claim 2,wherein the method includes iteratively performing the determining andthe evaluating, and wherein the method includes transitioning thecurrent route from a first route provided by a multimode transportationroute to a second route provided by a single mode transportation mode inwhich the user travels in a user auxiliary vehicle transportation mode,wherein iterations of the evaluating prior to the transitioning arecharacterized by evaluating multiple candidate routes based on themultimode transportation mode, and wherein iterations of the evaluatingsubsequent to the transitioning are characterized by evaluating multiplecandidate routes based on the single mode transportation mode.
 16. Thecomputer implemented method of claim 2, wherein the method includesiteratively performing the determining and the evaluating, and whereinthe method includes transitioning the current route from a first routeprovided by a multimode transportation route to a second route providedby a single mode transportation mode in which the user travels in anauxiliary vehicle mode of transportation, wherein iterations of theevaluating during a first period of time that occurs before thetransitioning are characterized by evaluating multiple candidate routesselected by a first process, and wherein iterations of the evaluatingduring a second period of time that occurs after the transitioning arecharacterized by evaluating multiple candidate routes selected by asecond process, wherein the first process is active during the firstperiod of time in response to iterations of the determining resulting ina determination that the user is traveling in the user passenger vehicletransportation modes, and wherein the second process is active duringthe second period of time in response to iterations of the determiningresulting in a determination that the user is traveling in a userauxiliary vehicle transportation mode.
 17. A computer implementedmethod, comprising: determining a current transportation mode of a userwith use of data of a user passenger vehicle associated to the user anddata of a user auxiliary passenger vehicle associated to the user,wherein the user passenger vehicle is capable of carrying the userauxiliary vehicle, and wherein the user auxiliary vehicle is configuredto be hand carried by the user; evaluating a current route of the userin dependence on the current transportation mode of the user asdetermined by the determining; and providing one or more output independence on the evaluating, wherein the evaluating a current routeincludes evaluating a predicted performance of the user passengervehicle through a geospatial region associated to a destination of theuser in comparison to a predicted performance of the user auxiliaryvehicle through the geospatial region associated to the destination ofthe user, and wherein the method includes for predicting performance ofthe user passenger vehicle and the user auxiliary vehicle through thegeospatial region include using a criteria for ascertaining predictedperformance of the user passenger vehicle and a user auxiliary vehicle,respectively, through the geospatial region, wherein the criteriaincludes one or more factor selected from the group consisting of F₁,F₂, F₃, F₄, and F₅, wherein F₁ is an average speed factor in dependenceon predicted average vehicle speed through the geospatial region, F₂ isa parking factor in dependence on predicted availability of vehicleparking within the geospatial region, F₃ is a comfort factor independence on predicted user comfort through the geospatial region, F₄is a physical exhaustion factor in dependence on predicted level ofphysical exhaustion of the user through the geospatial region, and F₅ isa current route factor which biases the selection of candidate routes infavor of re-selecting a current route.